Personalized event detection methods and related devices and systems

ABSTRACT

Medical devices and related patient management systems and event detection methods are provided. An exemplary method of detecting events pertaining to operation of a medical device, such as an infusion device, involves obtaining measurements indicative of a condition in a body of a patient, determining statistics for an analysis interval based on the measurements, and determining an event probability associated with the analysis interval based on historical event data associated with the patient. An event detection model associated with the patient is obtained and applied to the statistics and the event probability to identify occurrence of the event using the event detection model, and in response, an indication of the event associated with the analysis interval is provided, for example, by tagging or marking data in a database, displaying graphical indicia of the event, or the like.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application claims the benefit of the following United StatesProvisional Patent Applications: U.S. Provisional Patent ApplicationSer. No. 62/208,479, filed Aug. 21, 2015; U.S. Provisional PatentApplication Ser. No. 62/266,820, filed Dec. 14, 2015; U.S. ProvisionalPatent Application Ser. No. 62/286,828, filed Jan. 25, 2016; U.S.Provisional Patent Application Ser. No. 62/304,605, filed Mar. 7, 2016;U.S. Provisional Patent Application Ser. No. 62/304,609, filed Mar. 7,2016; U.S. Provisional Patent Application Ser. No. 62/304,615, filedMar. 7, 2016; U.S. Provisional Patent Application Ser. No. 62/304,618filed Mar. 7, 2016; and U.S. Provisional Patent Application Ser. No.62/329,021, filed Apr. 28, 2016.

TECHNICAL FIELD

Embodiments of the subject matter described herein relate generally tomedical devices, and more particularly, embodiments of the subjectmatter relate to autonomously detecting events for therapy management,analysis and reporting based on measurement data using personalized,patient-specific event detection models and related fluid infusiondevices and infusion systems.

BACKGROUND

Infusion pump devices and systems are relatively well known in themedical arts, for use in delivering or dispensing an agent, such asinsulin or another prescribed medication, to a patient. A typicalinfusion pump includes a pump drive system which typically includes asmall motor and drive train components that convert rotational motormotion to a translational displacement of a plunger (or stopper) in areservoir that delivers medication from the reservoir to the body of auser via a fluid path created between the reservoir and the body of auser. Use of infusion pump therapy has been increasing, especially fordelivering insulin for diabetics.

Control schemes have been developed that allow insulin infusion pumps tomonitor and regulate a user's blood glucose level in a substantiallycontinuous and autonomous manner. However, regulating blood glucoselevel is still complicated by variations in the response time for thetype of insulin being used along with variations in a user's individualinsulin response and daily activities (e.g., exercise, carbohydrateconsumption, bolus administration, and the like). Additionally,manually-initiated deliveries of insulin prior to or contemporaneouslywith consuming a meal (e.g., a meal bolus or correction bolus) alsoinfluence the overall glucose regulation, along with variouspatient-specific ratios, factors, or other control parameters.

Continuous monitoring provides a greater understanding of the conditionof a patient with diabetes. That said, there is also a burden imposed onpatients, physicians and other healthcare providers to adapt tocontinuous monitoring and incorporate the amount of available data in amanner that allows for improved patient outcomes. While meals can be amajor contributor to blood glucose variations, manual input is generallyrelied on to identify when meals occur and what the correspondingglucose response. However, many patients find manually indicating mealsto be unnecessarily burdensome. In other instances, a patient may simplyforget to provide a meal indication or be unable to provide a mealindication due to time or social constraints. Thus, for any givenpatient, a relatively large number of meals may occur without anyindication that facilitates further analysis or monitoring. Accordingly,there is a need to identify meals and corresponding glucose responseswithout relying on manual interaction.

BRIEF SUMMARY

Medical devices and related systems and operating methods are provided.An embodiment of a method of detecting events pertaining to operation ofa medical device associated with a patient involves obtaining, by acomputing device via a network, one or more measurements indicative of acondition in a body of the patient, determining, by the computingdevice, one or more statistics for an analysis interval based on the oneor more measurements, determining, by the computing device, an eventprobability associated with the analysis interval based on historicalevent data associated with the patient, obtaining, by the computingdevice, an event detection model associated with the patient,identifying, by the computing device, an event based on the one or morestatistics and the event probability using the event detection model,and providing, by the computing device, an indication of the eventassociated with the analysis interval. In one or more embodiments, theevent is a meal and the measurements are indicative of a glucose levelin the body of a patient, such as an individual with diabetes,dysglycemia, or some other condition.

In another embodiment, a method of detecting meals during to operationof an insulin infusion device associated with a patient is provided. Themethod involves obtaining sensor glucose measurements for the patient,obtaining a bolus history for the patient, obtaining a meal detectionmodel associated with the patient, the meal detection model identifyinga predictive subset of glucose measurement statistics correlative tomeals by the patient, calculating the predictive subset of glucosemeasurement statistics for an analysis interval of the sensor glucosemeasurements, determining a meal probability associated with theanalysis interval based on the bolus history, identifying a meal byapplying the meal detection model to the predictive subset of glucosemeasurement statistics for the analysis interval and the mealprobability associated with the analysis interval, and in response toidentifying the meal, providing an indication of the meal associatedwith the analysis interval.

In another embodiment, a system is provided that includes a sensingarrangement to obtain measurement values for a physiological conditionin a body of a patient, a database to store historical event data and anevent detection model associated with the patient, and a computingdevice communicatively coupled to the database and a network. Thecomputing device is configured to obtain the measurement values,determine one or more statistics for an analysis interval based on themeasurement values, determine an event probability associated with theanalysis interval based on the historical event data associated with thepatient, apply the event detection model to the one or more statisticsand the event probability to detect an event associated with theanalysis interval, and provide an indication of the event associatedwith the analysis interval.

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the detaileddescription. This summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the subject matter may be derived byreferring to the detailed description and claims when considered inconjunction with the following figures, wherein like reference numbersrefer to similar elements throughout the figures, which may beillustrated for simplicity and clarity and are not necessarily drawn toscale.

FIG. 1 depicts an exemplary embodiment of a patient management system;

FIG. 2 is a flow diagram of an exemplary patient modeling processsuitable for use with the patient management system of FIG. 1 in one ormore exemplary embodiments;

FIG. 3 is a graph depicting an exemplary relationship of sensor glucosemeasurement values with respect to time in accordance with oneembodiment suitable for use with the patient modeling process of FIG. 2in one or more exemplary embodiments;

FIG. 4 is a graph depicting an exemplary relationship of patient mealprobability with respect to time in accordance with one embodimentsuitable for use with the patient modeling process of FIG. 2 in one ormore exemplary embodiments;

FIG. 5 is a table depicting an exemplary meal detection models generatedfor different patients in conjunction with the patient modeling processof FIG. 2 in one or more exemplary embodiments;

FIG. 6 is a flow diagram of an exemplary meal detection process suitablefor use with a meal detection model generated by the patient modelingprocess of FIG. 2 in the patient management system of FIG. 1 in one ormore exemplary embodiments;

FIG. 7 depicts an exemplary embodiment of a snapshot graphical userinterface (GUI) display that may be presented on a display deviceassociated with a computing device in one or more embodiments;

FIG. 8 depicts an embodiment of a computing device for a diabetes datamanagement system in accordance with one or more embodiments;

FIG. 9 depicts an exemplary embodiment of an infusion system;

FIG. 10 depicts a plan view of an exemplary embodiment of a fluidinfusion device suitable for use in the infusion system of FIG. 9;

FIG. 11 is an exploded perspective view of the fluid infusion device ofFIG. 10;

FIG. 12 is a cross-sectional view of the fluid infusion device of FIGS.10-11 as viewed along line 12-12 in FIG. 11 when assembled with areservoir inserted in the infusion device;

FIG. 13 is a block diagram of an exemplary control system suitable foruse in a fluid infusion device in one or more embodiments;

FIG. 14 is a block diagram of an exemplary pump control system suitablefor use in the control system of FIG. 13 in one or more embodiments; and

FIG. 15 is a block diagram of a closed-loop control system that may beimplemented or otherwise supported by the pump control system in thefluid infusion device of FIG. 13 in one or more exemplary embodiments.

DETAILED DESCRIPTION

The following detailed description is merely illustrative in nature andis not intended to limit the embodiments of the subject matter or theapplication and uses of such embodiments. As used herein, the word“exemplary” means “serving as an example, instance, or illustration.”Any implementation described herein as exemplary is not necessarily tobe construed as preferred or advantageous over other implementations.Furthermore, there is no intention to be bound by any expressed orimplied theory presented in the preceding technical field, background,brief summary or the following detailed description.

Exemplary embodiments of the subject matter described herein areimplemented in conjunction with medical devices, such as portableelectronic medical devices. Although many different applications arepossible, the following description focuses on embodiments thatincorporate a fluid infusion device (or infusion pump) as part of aninfusion system deployment. For the sake of brevity, conventionaltechniques related to infusion system operation, insulin pump and/orinfusion set operation, and other functional aspects of the systems (andthe individual operating components of the systems) may not be describedin detail here. Examples of infusion pumps may be of the type describedin, but not limited to, U.S. Pat. Nos. 4,562,751; 4,685,903; 5,080,653;5,505,709; 5,097,122; 6,485,465; 6,554,798; 6,558,320; 6,558,351;6,641,533; 6,659,980; 6,752,787; 6,817,990; 6,932,584; and 7,621,893;each of which are herein incorporated by reference. That said, thesubject matter described herein can be utilized more generally in thecontext of overall diabetes management or other physiological conditionsindependent of or without the use of an infusion device or other medicaldevice (e.g., when oral medication is utilized), and the subject matterdescribed herein is not limited to any particular type of medication.

Generally, a fluid infusion device includes a motor or other actuationarrangement that is operable to linearly displace a plunger (or stopper)of a reservoir provided within the fluid infusion device to deliver adosage of fluid, such as insulin, to the body of a user. Dosage commandsthat govern operation of the motor may be generated in an automatedmanner in accordance with the delivery control scheme associated with aparticular operating mode, and the dosage commands may be generated in amanner that is influenced by a current (or most recent) measurement of aphysiological condition in the body of the user. For example, in aclosed-loop operating mode, dosage commands may be generated based on adifference between a current (or most recent) measurement of theinterstitial fluid glucose level in the body of the user and a target(or reference) glucose value. In this regard, the rate of infusion mayvary as the difference between a current measurement value and thetarget measurement value fluctuates. For purposes of explanation, thesubject matter is described herein in the context of the infused fluidbeing insulin for regulating a glucose level of a user (or patient);however, it should be appreciated that many other fluids may beadministered through infusion, and the subject matter described hereinis not necessarily limited to use with insulin.

Exemplary embodiments described herein generally relate to systems foranalyzing and presenting information pertaining to operation of aninfusion device delivering fluid to a body of a user, and in particular,information pertaining to events occurring during operation. Forexample, in one or more embodiments, a snapshot graphical user interface(GUI) display may be presented that includes graphical indicia of eventpattern(s) identified during preceding operation of the infusion device.The event pattern information may be utilized by the patient, thepatient's doctor or other health care provider, or another individual toassess the efficacy of the regulation achieved by the infusion device,identify potential causes of an event pattern, and identify potentialactions that may improve the quality of control achieved by the infusiondevice.

In exemplary embodiments described herein, events occurring duringoperation of an infusion device are capable of being detectedautonomously and without reliance on any manual interaction or inputsusing a personalized, patient-specific event detection model. The eventdetection model relies on input variables that have been identified aspredictive or correlative to an occurrence of the event for thatpatient, which may include patient-specific event probabilitiesdetermined based on historical event data associated with the patientand one or more measurement statistics determined based on measurementdata for a physiological condition of the patient. In this regard, foreach patient, the particular subset of measurement statistics that arepredictive or correlative to the event for that patient may be differentfrom those of other patients, and similarly, whether the historicalevent probability is predictive or correlative to the event may alsovary on a patient-by-patient basis. Additionally, the patient-specificevent detection model also utilizes correlation coefficient values toweight the relative predictiveness or correlative strength of each modelinput variable. Thus, the model determines a metric indicative of thelikelihood of an occurrence or an absence of the event based on thepatient-specific predictive subset of measurement statistics andcorresponding patient-specific correlation coefficient values. In someembodiments, events may be identified substantially in real-time.

In exemplary embodiments, the events to be detected are meals occurringduring monitoring of a patient's glucose levels and correspondingoperation of an insulin infusion device. In this regard, apatient-specific meal detection model is employed to calculate orotherwise determine a metric indicative of whether or not a mealcorresponding to a current analysis interval has occurred based on asubset of glucose measurement statistics that have been identified aspredictive of or correlative to consumption of a meal by the patientbased on the patient's historical meal and bolus information. For somepatients, the meal detection model may also incorporate a mealprobability metric for the current analysis interval that reflects thelikelihood of the patient consuming a meal contemporaneously orconcurrently to the current analysis interval based on the patient'shistorical meal distribution. Values for the predictive subset ofglucose measurement statistics are calculated based on glucosemeasurement data corresponding to the current analysis interval and ameal probability metric for the current analysis interval is alsodetermined, and the respective correlation coefficients from the mealdetection model are applied to the current values for the predictivesubset of glucose measurement statistics and the current mealprobability to obtain a meal consumption metric value. The mealconsumption metric value is then utilized to determine whether a mealhas been consumed during the current analysis interval.

When consumption of a meal is detected, the glucose measurement dataassociated with the current analysis interval may be flagged, marked, orotherwise stored in association with an identifier that indicates a mealassociated with the analysis interval corresponding to that subset ofthe glucose measurement data. Accordingly, any notifications, alerts,displays, or other actions that would otherwise be undertaken inresponse to a manually-indicated or announced meal may be similarlyperformed in response to or in a manner that is influenced by a mealdetected by the meal detection model. Thus, meals may be autonomouslyidentified and automatically reflected by subsequent operation of theinfusion device or displays pertaining thereto without requiring anymanual interaction. Not only is the patient alleviated of the burdensassociated with manually announcing meals, but inadvertently omittedmeal indications may also be accounted for. As a result, the number ofmeals for which data may be captured and analyzed may increase, which,in turn, may increase the accuracy or reliability of mealresponse-related analysis, and thereby improve patient outcomes.

FIG. 1 depicts an exemplary embodiment of a patient management system100. The patient management system 100 includes an infusion device 102that is communicatively coupled to a sensing arrangement 104 to obtainmeasurement data indicative of a physiological condition in the body ofa patient, such as sensor glucose measurement values, as described ingreater detail below in the context of FIGS. 9-12. In exemplaryembodiments, the infusion device 102 operates autonomously to regulatethe patient's glucose level based on the sensor glucose measurementvalues received from the sensing arrangement 104 as described in greaterdetail below in the context of FIGS. 13-15.

In the illustrated embodiment, the infusion device 102 periodicallyuploads or otherwise transmits the measurement data (e.g., sensorglucose measurement values and timestamps associated therewith) to aremote device 106 via a communications network 114, such as a wiredand/or wireless computer network, a cellular network, a mobile broadbandnetwork, a radio network, or the like. That said, in other embodiments,the sensing arrangement 104 may be communicatively coupled to thecommunications network 114 to periodically upload or otherwise transmitmeasurement data to the remote device 106 via the communications network114 independent of the infusion device 102. Additionally, in someembodiments, the infusion device 102 also uploads delivery data and/orother information indicative of the amount of fluid delivered by theinfusion device and the timing of fluid delivery, which may include, forexample, information pertaining to the amount and timing ofmanually-initiated boluses and associated meal announcements. Someexamples of an infusion device uploading measurement and delivery datato a remote device are described in United States Patent ApplicationPublication Nos. 2015/0057807 and 2015/0057634, which are incorporatedby reference herein in their entirety.

The remote device 106 is coupled to a database 108 configured to storeor otherwise maintain the historical measurement and delivery datareceived from the infusion device 102 in association with a patientassociated with the infusion device 102 (e.g., using unique patientidentification information). Additionally, the database 108 may store orotherwise maintain, in association with a particular patient, apersonalized and patient-specific event detection model. In this regard,the event detection model defines which input variables or parametersare to be factored in when making a determination of whether or not anevent has occurred along with relative weightings assigned to thoseinputs corresponding to how predictive or correlative the value of arespective model input value is to the occurrence of the event to bedetected, as described in greater detail below in the context of FIGS.2-6. The remote device 106 generally represents an electronic deviceconfigured to analyze or otherwise monitor the measurement and deliverydata obtained for the patient associated with the infusion device 102,generate patient-specific event detection models based on a respectivepatient's historical measurement and delivery data, and generate orotherwise facilitate a GUI display that is influenced by or otherwisereflects the detected events. The GUI display may be presented on theremote device 106 or another electronic device 110, alternativelyreferred to herein as a client device. In practice, the remote device106 may reside at a location that is physically distinct and/or separatefrom the infusion device 102, such as, for example, at a facility thatis owned and/or operated by or otherwise affiliated with a manufacturerof the infusion device 102. For purposes of explanation, but withoutlimitation, the remote device 106 may alternatively be referred toherein as a server.

The remote device 106 generally represents a computing system or anothercombination of processing logic, circuitry, hardware, and/or othercomponents configured to support the processes, tasks, operations,and/or functions described herein. In this regard, the server 106includes a processing system 116, which may be implemented using anysuitable processing system and/or device, such as, for example, one ormore processors, central processing units (CPUs), controllers,microprocessors, microcontrollers, processing cores and/or otherhardware computing resources configured to support the operation of theprocessing system 116 described herein. The processing system 116 mayinclude or otherwise access a data storage element 118 (or memory)capable of storing programming instructions for execution by theprocessing system 116, that, when read and executed, cause processingsystem 116 to perform or otherwise support the processes, tasks,operations, and/or functions described herein. For example, in oneembodiment, the instructions cause the processing system 116 to create,generate, or otherwise facilitate an application platform that supportsinstances of an application using data that is stored or otherwisemaintained by the database 108. Depending on the embodiment, the memory118 may be realized as a random access memory (RAM), read only memory(ROM), flash memory, magnetic or optical mass storage, or any othersuitable non-transitory short or long term data storage or othercomputer-readable media, and/or any suitable combination thereof.

The client device 110 generally represents an electronic device coupledto the network 114 that may be utilized by a user to access and viewdata stored in the database 108 via the server 106. In practice, theclient device 110 can be realized as any sort of personal computer,mobile telephone, tablet or other network-enabled electronic device thatincludes a display device, such as a monitor, screen, or anotherconventional electronic display, capable of graphically presenting dataand/or information provided by the server 106 along with a user inputdevice, such as a keyboard, a mouse, a touchscreen, or the like, capableof receiving input data and/or other information from the user of theclient device 110. A user, such as the patient's doctor or anotherhealthcare provider, manipulates the client device 110 to execute aclient application 112 that contacts the server 106 via the network 114using a networking protocol, such as the hypertext transport protocol(HTTP) or the like.

In exemplary embodiments described herein, a user of the client device110 manipulates a user input device associated with the client device110 to input or otherwise provide indication of the patient associatedwith the infusion device 102 along with a period of time for which theuser would like to review, analyze, or otherwise assess measurement dataassociated with the patient. In response, the server 106 accesses thedatabase 108 to retrieve or otherwise obtain historical measurement dataassociated with the identified patient for the identified time periodand generates a GUI display that is presented on the display deviceassociated with the client device 110 via the client application 112executing thereon.

It should be appreciated that FIG. 1 depicts a simplified representationof a patient management system 100 for purposes of explanation and isnot intended to limit the subject matter described herein in any way.For example, in various embodiments, a GUI display may be presented onany device within the patient management system 100 (e.g., the server106, the infusion device 102, the sensing arrangement 104, or the like)and not necessarily on the client device 110. Moreover, in someembodiments, the infusion device 102 may be configured to store orotherwise maintain historical measurement and delivery data onboard theinfusion device 102 and generate GUI displays on a display deviceassociated with the infusion device 102, in which case one or more ofthe server 106, the database 108, and the client device 110 may not bepresent. Additionally, the subject matter described herein is notlimited to meal detection or event detection for purposes of generatingGUI displays, and in various embodiments, meal or event detection may beutilized to dynamically adjust operation or otherwise influence thecontrol scheme of the infusion device 102 (e.g., by adjusting one ormore thresholds or parameters influencing delivery, notifications,alerting, or the like), generate alerts or other user notifications, orperform other actions in response to the detected meal or event.

FIG. 2 depicts an exemplary patient modeling process 200 suitable forimplementation by a patient management system to develop apatient-specific event detection model. The various tasks performed inconnection with the patient modeling process 200 may be performed byhardware, firmware, software executed by processing circuitry, or anycombination thereof. For illustrative purposes, the followingdescription refers to elements mentioned above in connection withFIG. 1. In practice, portions of the patient modeling process 200 may beperformed by different elements of the patient management system 100,such as, for example, the infusion device 102, the sensing arrangement104, the server 106, the database 108, the client device 110, the clientapplication 112, and/or the processing system 116. It should beappreciated that the patient modeling process 200 may include any numberof additional or alternative tasks, the tasks need not be performed inthe illustrated order and/or the tasks may be performed concurrently,and/or the patient modeling process 200 may be incorporated into a morecomprehensive procedure or process having additional functionality notdescribed in detail herein. Moreover, one or more of the tasks shown anddescribed in the context of FIG. 2 could be omitted from a practicalembodiment of the patient modeling process 200 as long as the intendedoverall functionality remains intact.

The patient modeling process 200 begins by obtaining historicalmeasurement data for the patient of interest and obtaining historicalbolus data for the patient over the period corresponding to thehistorical measurement data (tasks 202, 204). In this regard, thepatient modeling process 200 may be initiated or performed after theserver 106 has acquired and stored sufficient sensor glucose measurementvalues and bolus information in the database 108. For example, theinfusion device 102 may periodically upload, to the server 106 via thenetwork 114, reference blood glucose measurement values obtained fromthe body of the patient (e.g., using a blood glucose meter orfingerstick device) along with bolus information including the timingsand amounts of insulin delivered, including indications of whether aparticular bolus is a meal bolus or otherwise associated with a meal.The bolus information may also include the amount of carbohydratesconsumed, the type of meal, or the like. In this regard, in the absenceof an explicit meal indication or announcement from the patient, theserver 106 may automatically classify a bolus delivered as a meal boluswhen a carbohydrate entry occurred within a threshold amount of time ofthe bolus being delivered (e.g., within 5 minutes). Additionally, theinfusion device 102 (or alternatively, the sensing arrangement 104) mayperiodically upload, to the server 106, sensor glucose measurementvalues obtained from the body of the patient by the sensing arrangement104.

In one or more embodiments, the server 106 stores or otherwisemaintains, in the database 108, one or more files or entries associatedwith the patient that maintains an association between the patient'shistorical sensor glucose measurement data, the patient's historicalbolus and meal data, the patient's historical reference blood glucosemeasurements, and the like. Once a sufficient amount of data has beenobtained by the server 106 and/or the database 108, the patient modelingprocess 200 continues with determining a patient-specific meal detectionmodel based on the patient's historical sensor glucose measurement andbolus data. In one embodiment, the patient modeling process 200 requiresthat data for at least a minimum threshold number of days (or hours) hasbeen uploaded to continue. In other embodiments, additional thresholdsmay be utilized to determine when modeling can occur, such as, forexample, a minimum average number of meal boluses per day of data, aminimum average number of reference blood glucose values per day ofdata, or the like.

In some embodiments, the server 106 also performs or otherwiseimplements one or more data cleaning or data preparation processes onthe data to reduce noise or transients within the data, such as, forexample, low-pass filtering the sensor glucose measurement values orotherwise filtering, excluding, or mitigating the impact of portions ofdata accompanied by variables that could cause variations that wouldreduce accuracy or reliability of the model. For example, the server 106may identify and reduce or otherwise eliminate the impact of dataassociated with periods of time involving anomalous measurement data,such as, for example, artifacts, dropouts, or the like. In someembodiments, the measurement data associated with or potentiallyinfluenced by artifacts, dropouts or other anomalous sensor behavior maybe detected by either the sensing arrangement 104 or the infusion device102 instead of the server 106. Examples of artifact or dropout detectionare described in U.S. Patent Application Pub. No. 2015/0328402 andincorporated by reference herein. In other embodiments, the datacleaning or filtering may also account for suspension of delivery by theinfusion device 102 or meals accompanied by stacked boluses, anomalousor unusual carbohydrate amounts, anomalous or unusual bolus dosages,particular bolus types, anomalous or unusual carbohydrate ratios,anomalous or unusual sensitivity factors, anomalous or unusual glucosetargets, anomalous or unusual basal rates, anomalous or unusual activeinsulin amounts, or the like.

Once sufficient historical measurement and bolus data is obtained, thepatient modeling process 200 continues by classifying or categorizingsegments of time within the period of time encompassed by the historicaldata into meal or non-meal time segments (task 206). In an exemplaryembodiment, a meal segment is defined as a one hour segment ofhistorical sensor glucose measurement values that starts around the timeof a meal bolus (at, before or after) and precedes the highest ormaximum sensor glucose measurement value within the 90 minutes followingthe meal bolus, where that meal bolus is also associated with acarbohydrate amount greater than a minimum threshold (e.g., 0 grams), isdelivered within a maximum amount of time difference (e.g., 5 minutes)before or after the carbohydrate entry, and is not preceded by anothermeal bolus within 3 hours of the reference meal bolus associated withthe meal segment. In this regard, the threshold criteria are intended todiminish the effects of active insulin from other boluses and ensure themeal segments reliably reflect a characteristic meal response of thepatient. In exemplary embodiments, a non-meal segment is defined as aone hour segment of historical sensor glucose measurement values thatprecedes or follows a meal segment. Historical sensor glucosemeasurement values that are not classified into a meal or non-mealsegment are not utilized for purposes of generating the model due tothose segments of data potentially exhibiting responses to residualactive insulin from preceding meal boluses, as well as maintainingsegments of consistent duration. In one or more embodiments where anabsolute timestamp is available for when a patient begins eating a meal(e.g., by a user indication via the client application 112), a mealsegment may be defined as the one hour segment of historical sensorglucose measurement values that starts at that time.

FIG. 3 depicts an exemplary graph 300 of sensor glucose measurementvalues with respect to time illustrating one example of how sensorglucose measurement data could be classified into different meal ornon-meal segments based on meal bolus indications and the sensor glucosemeasurement values. In the illustrated example, segments 302, 304, 306of sensor glucose measurement values around meal bolus indications 301,303, 305 are classified as meal segments, with segments 310, 312, 314,316 and 318 preceding the respective meal segments 302, 304, 306 beingclassified as non-meal segments. Here, it is noted that interveningsegments 320, 322, 324, 326 between meal segments 302, 304 and non-mealsegments 312, 314, 316 do not satisfy the criteria for being classifiedas either a meal or non-meal segment, and therefore, are excluded fromfurther use in developing a meal detection model.

Referring again to FIG. 2, after classifying the historical sensorglucose measurement data for the patient into meal and non-mealsegments, the patient modeling process 200 continues by calculating orotherwise determining sensor glucose measurement statistics associatedwith the respective segments based on the sensor glucose measurementvalues corresponding to that respective segment (task 208). In thisregard, the sensor glucose measurement statistics characterize thepatient's glucose level within or during the respective segment. Forexample, in one embodiment, for each one hour meal or non-meal segment,the server 106 calculates a mean or average sensor glucose measurementvalue over that one hour period, a standard deviation of the sensorglucose measurement values during that one hour period, a mean oraverage rate of change of the sensor glucose measurement values on asample-to-sample basis during that one hour period, mean or average rateof changes of the sensor glucose measurement values on asample-to-sample basis for discrete time periods within that one hourperiod (e.g., within the first 30 minutes and the second 30 minutes),standard deviations associated with those rate of change values, anabsolute amplitude of the sensor glucose measurement values during thatsegment (e.g., the difference between the maximum and minimum valueswithin that segment), and an amplitude difference between the first andlast glucose measurement value of that segment (e.g., by subtracting thefirst glucose measurement value from the last glucose measurementvalue).

The patient modeling process 200 also calculates or otherwise determinesa meal probability associated with the respective segments based on thepatient's bolus history (task 210). In this regard, the server 106analyzes the meal bolus indications associated with the patient todetermine a meal probability distribution for the patient thatrepresents the relative probability of the patient consuming a meal at aparticular time of day. In one embodiment, the server 106 divides theperiod of a day into a plurality of intervals, with a respective mealprobability associated with each interval being determined based on thenumber of meal bolus indications having an associated timestamp withinthat interval relative. For example, FIG. 4 depicts an exemplary mealdistribution 400 with respect to the time of day divided into 30 minuteintervals, resulting in 48 different intervals over a 24-hour period.The corresponding meal probability for each interval may be determinedby dividing the number of meal boluses classified or assigned to thatrespective interval by the total number of meal boluses. Thereafter,each meal or non-meal segment may be assigned a corresponding mealprobability value based on the corresponding intervals overlapped orconcurrent to a respective segment.

In one embodiment, meal bolus instances for a patient are grouped into10 minute intervals, resulting in 144 different intervals over a 24-hourperiod. For example, for a meal segment 302 spanning from 11:30 AM to12:30 PM, the server 106 may determine a meal probability associatedwith that meal segment 302 as a sum of the number of instances of mealboluses with the six ten minute intervals encompassed by the mealsegment (e.g., the 11:30 AM-11:40 AM interval, the 11:40 AM-11:50 AMinterval, the 11:50 AM-12:00 PM interval, and so on) and then dividingthe resultant sum by the total number of instances of meal boluses inthe patient's history. It should be noted that 10 minute intervals aredepicted and described for purposes of explanation, but in practice, themeal probability intervals are not limited to any particular length orduration, and may be shorter or longer than 10 minutes to achieve adesired level of granularity.

Still referring to FIG. 2, after determining the sensor glucosemeasurement statistics and meal probability values associated with eachrespective meal segment or non-meal segment, the patient modelingprocess 200 continues by identifying a subset of the sensor glucosemeasurement statistics and meal probability values that are predictiveor correlative to the occurrence of a meal for that individual patientand generating a patient-specific meal detection model for that patientusing that predictive subset of variables (tasks 212, 214). In thisregard, in exemplary embodiments, the server 106 utilizes machinelearning to determine which of the sensor glucose measurement statisticsand the meal probability value are most strongly correlated to orpredictive of each segment being of the classified type, and thendetermines a corresponding equation for calculating a metric indicativeof whether or not a segment is a meal segment based on that subset ofvariables. Thus, the model is capable of characterizing or mapping thepatient's response evidenced by the sensor glucose measurement values tothat of a meal, and vice versa. Since each patient's response may varyfrom the rest of the population, the subset of sensor glucosemeasurement statistics that are predictive of or correlative to a mealfor that patient may vary from other users. Additionally, the relativeweightings applied to the respective statistics of that predictivesubset may also vary from other patient's who may have common predictivesubsets, based on differing correlations between a particular glucosemeasurement statistic and the meal response of that particular patient.

In one embodiment, stepwise logistic regression is utilized by theserver 106 to determine what sensor glucose measurement statistics amongthe plurality of sensor glucose measurement statistics determined attask 208 are predictive of a meal for the patient of interest, as wellas identifying whether the meal probability values determined at task210 for that patient are predictive. In this regard, the mealprobability value may not be predictive or correlative for a patientthat grazes or consumes meals throughout the day, while the mealprobability value may be highly predictive or correlative for a patientthat grazes or consumes meals at relatively defined times of day. Insome embodiments, the relative strength of correlation for each of thosepredictive variables may be utilized to determining a weighting orscaling for the values of those variables to arrive at an output fromthe model (e.g., by applying those weightings to those variable values)that indicates the occurrence or non-occurrence of a meal during ananalysis interval (e.g., a one hour segment of sensor glucosemeasurement values). In other embodiments, multiple predictive variablesmay be multiplied or otherwise factored in with one another when valuesfor those variables, in combination, are predictive or correlative to ameal.

In one or more exemplary embodiments, only a subset of the meal andnon-meal segments for the patient are used to develop the meal detectionmodel, with the remaining the meal and non-meal segments being utilizedby the patient modeling process 200 to test or otherwise validate thedeveloped model (tasks 216, 218). For example, for segments classifiedinto the testing group of segments, the server 106 applies the developedmeal detection model to the predictive variable values associated withthe respective test segment, and then identifies or otherwise determineswhether the model results in that test segment being classified as ameal segment or non-meal segment. Thereafter, the server 106 comparesthe model classification of each test segment to the previousclassification (e.g., task 206) and calculates or otherwise determinesone or more metrics indicative of the performance of the model withrespect to the test segments. For example, the server 106 may determinean accuracy metric associated with the model based on the number oftimes the model correctly classified a test segment as a meal ornon-meal segment. The server 106 may also calculate a sensitivity metricassociated with the model as the ratio of the correctly detected numberof meals by the model relative to the total number of meal segments inthe test group. The server 106 may also calculate a specificity metricassociated with the model as the ratio of the correctly detected numberof non-meal segments by the model relative to the total number ofnon-meal segments in the test group.

When the performance metrics associated with the developed model aregreater than or otherwise satisfy applicable validation criteria, thepatient modeling process 200 stores or otherwise maintains the model inassociation with the patient for use in subsequently detecting mealsthat are not manually indicated or announced by the patient (task 220).For example, identification of the predictive variables for the patientalong with the relative weightings or manner in which those predictivevariables should be combined to obtain a metric indicative of a meal maybe stored or otherwise maintained in the database 108 in associationwith a patient identifier assigned to or otherwise associated with thepatient and/or the infusion device 102. Conversely, when the performancemetrics associated with the developed model do not satisfy applicablevalidation criteria, the patient modeling process 200 discards thedeveloped model and assigns or otherwise associates the patient with abroader population model (task 222). In this regard, the populationmodel may be developed by performing various aspects of the patientmodeling process 200 across meal and non-meal segments associated with aplurality of different patients. For example, in one embodiment, apatient may be assigned or otherwise associated with a particular groupof patients having one or more characteristics in common, with a mealdetection model for that patient group being determined based on mealand non-meal segments for the different patients of the group. In one ormore embodiments, the patient modeling process 200 assigns or otherwiseassociates the patient with a patient group meal detection model uponinitialization of the patient within the patient management system 100prior to accumulating sufficient historical data for developing apatient-specific model.

FIG. 5 depicts a table 500 illustrating different meal detection modelsgenerated for different patients. A first patient (Patient 1) has 364days worth of historical measurement and bolus data stored in thedatabase 108 that includes 72,660 instances of sensor glucosemeasurement values that can be classified into 1028 meal segments and4936 non-meal segments, which are further divided into a training groupof segments (consisting of 813 meal segments and 3833 non-meal segments)and a test group of segments (consisting of 215 meal segments and 1103non-meal segments). The server 106 analyzes the test group of segmentsand determines a patient-specific meal detection model 502 for Patient 1that generates a metric indicative of a meal occurrence based on thepredictive variables (or model inputs) of: mean sensor glucose rate ofchange over the preceding one hour of sensor glucose measurement values,mean sensor glucose rate of change over the first half hour of thepreceding one hour of sensor glucose measurement values, the mealprobability value associated with the preceding one hour interval, theproduct of the timestamp associated with the meal and the amplitudedifference of the sensor glucose measurement value over the precedingone hour interval, and the product of the mean and standard deviation ofthe sensor glucose rate of change over the second half hour of thepreceding one hour of sensor glucose measurement values. Afterdetermining the model 502, the server 106 applies the model 502 to thetesting group of segments and then determines performance metricsassociated with the meal/non-meal outputs model 502. When theperformance metrics satisfy validation criteria, the server 106 storesor otherwise maintains information defining the model 502 in thedatabase 108 in association with Patient 1.

A second patient (Patient 2) has 5 days worth of historical measurementand bolus data stored in the database 108 that includes 967 instances ofsensor glucose measurement values that can be classified into 10 mealsegments and 70 non-meal segments, which are further divided into atraining group of segments (consisting of 5 meal segments and 55non-meal segments) and a test group of segments (consisting of 5 mealsegments and 15 non-meal segments). The server 106 analyzes the testgroup of segments and determines a patient-specific meal detection model504 for Patient 2 that generates a metric indicative of a mealoccurrence based on the predictive variables (or model inputs) of:standard deviation of the sensor glucose rate of change over the firsthalf hour of the preceding one hour of sensor glucose measurement valuesand the meal probability value associated with the one hour interval.After determining the model 504, the server 106 applies the model 504 tothe testing group of segments and then determines performance metricsassociated with the meal/non-meal outputs of the model 504. In thisregard, if the performance metrics satisfy validation criteria, theserver 106 discards the model 504 or otherwise fails to store the model504 in association with Patient 2. For example, if the validationcriteria are an accuracy greater than 95%, a sensitivity greater than80%, and a specificity greater than 80%, the Patient 1 model 502 may bevalidated and stored in association with Patient 1 while the Patient 2model 504 fails the validation and is discarded or otherwise ignored infavor of a more generic population model for Patient 2.

FIG. 6 depicts an exemplary meal detection process 600 suitable forimplementation by a patient management system to detect or otherwiseidentify unannounced meals using a patient-specific meal detectionmodel. The various tasks performed in connection with the meal detectionprocess 600 may be performed by hardware, firmware, software executed byprocessing circuitry, or any combination thereof. For illustrativepurposes, the following description refers to elements mentioned abovein connection with FIG. 1. In practice, portions of the meal detectionprocess 600 may be performed by different elements of the patientmanagement system 100, such as, for example, the infusion device 102,the sensing arrangement 104, the server 106, the database 108, theclient device 110, the client application 112, and/or the processingsystem 116. It should be appreciated that the meal detection process 600may include any number of additional or alternative tasks, the tasksneed not be performed in the illustrated order and/or the tasks may beperformed concurrently, and/or the meal detection process 600 may beincorporated into a more comprehensive procedure or process havingadditional functionality not described in detail herein. Moreover, oneor more of the tasks shown and described in the context of FIG. 6 couldbe omitted from a practical embodiment of the meal detection process 600as long as the intended overall functionality remains intact.

In exemplary embodiments, the meal detection process 600 is implementedor otherwise performed by server 106, for example, to detect orotherwise identify unannounced meals to facilitate generating GUIdisplays, notifications or alerts, or otherwise analyzing a patient'smeal response. In some embodiments, the meal detection process 600 maybe performed by the server 106 in response to receiving uploadedmeasurement data from the infusion device 102 and/or the sensingarrangement 104 to detect or otherwise identify a meal substantially inreal-time. In other embodiments, the meal detection process 600 may beperformed on a periodic basis independent of the infusion device 102and/or the sensing arrangement 104, for example, on a daily basis, aweekly basis, a monthly basis, or the like.

The meal detection process 600 begins by receiving or otherwiseobtaining sensor glucose measurement values associated with a patient,determining sensor glucose measurement statistics associated with apreceding analysis interval, and determining a meal probability valuefor the preceding analysis interval (tasks 602, 604, 606). In thisregard, the server 106 obtains sensor glucose measurement values thatare not accompanied by a meal indication, and calculates or otherwisedetermines sensor glucose measurement statistics based on the sensorglucose measurement values corresponding to an analysis intervalpreceding the latest or most recent measurement value. For example,continuing the above example, the server 106 calculates sensor glucosemeasurement statistics based on the sensor glucose measurement valuesfor the hour preceding the current sensor glucose measurement valuebeing analyzed in a similar manner as described above (e.g., task 208).The server 106 also determines a meal probability value based on anaverage of the meal probability intervals over the one hour intervalpreceding the current sensor glucose measurement value in a similarmanner as described above (e.g., task 210).

After determining sensor glucose measurement statistics and a mealprobability value for an interval of sensor glucose measurement valuesto be analyzed, the meal detection process 600 obtains the mealdetection model associated with the patient being analyzed and appliesthe detection model to the predictive subset of the sensor glucosemeasurement statistics and meal probability value to determine a metricindicative of whether the current analysis interval corresponds to ameal or non-meal segment (tasks 608, 610). In this regard, the server106 implements the equation defined by the meal detection modelassociated with the identifier(s) for the patient being analyzed usingcurrent values for the predictive sensor glucose measurement statisticsubset identified by the model and the current meal probability value(if indicated as predictive by the model) to obtain a result thatquantifies or otherwise characterizes the likelihood of the patienthaving consumed a meal concurrently or contemporaneously to the currentanalysis interval. Alternatively, in instances where the patient doesnot have a patient-specific model assigned, the server 106 may utilize apatient group meal detection model corresponding to a patient group thepatient is associated with.

Based on the output of the model, the meal detection process 600determines whether the current analysis interval corresponds to a mealresponse by the patient being analyzed, and in response to detecting ameal, the meal detection process 600 provides indication of a mealassociated with the current analysis interval (tasks 612, 614). In someembodiments, when the model output indicates the current analysisinterval corresponds to a meal segment, the server 106 may analyze thepatient's bolus data corresponding to the analysis interval to verifywhether a meal bolus identified by the patient (e.g., a bolusaccompanied by an input carbohydrate amount or other identification of ameal) was delivered during the analysis interval. When the patient'sbolus data includes a bolus delivered during the analysis interval thatis not marked as a meal bolus, the server 106 may flag, mark, orotherwise store an indication of a meal bolus in association with thatbolus. Additionally, in some embodiments, where the model is capable ofbeing utilized to classify a type of bolus delivered, the server 106 mayalso store an indication of a bolus type associated with that mealbolus. In some embodiments, in the absence of any bolus delivered duringthe analysis interval, the server 106 may store an indication of a bolusin association with one or more sensor glucose measurement values or thetime period corresponding to the analysis interval to thereby flag, tag,or otherwise mark the analysis interval as a meal segment with acorresponding meal having been consumed by the patient. For example, inone embodiment, the server 106 calculates, estimates, or otherwisedetermines a time associated with consumption of the meal based on thesensor glucose measurements of the analysis interval and then creates orotherwise generates a meal indication associated with the patient havingan associated timestamp corresponding to the estimated time of mealconsumption. In this regard, the server 106 may update the patient'shistorical bolus data to indicate the meal associated with the analysisinterval that was detected by the server 106 using the patient's mealdetection model.

In response to detecting a meal, in addition to updating the patient'sbolus history, the server 106 may update one or more GUI displays toreflect the meal, generate an alert or user notification based on themeal, or initiate or perform some other action in response to the meal.For example, if a monitoring GUI display depicting a graphicalrepresentation of the patient's sensor glucose is presented on a device102, 110 within the patient management system 100, the server 106 mayupdate the GUI display to include a marker for a meal bolus at a timecorresponding to the analysis interval, such as for example, at the timeof a sensor glucose measurement value of the analysis interval (e.g.,the time of the lowest sensor glucose measurement value) or anothertimestamp assigned to the meal bolus. Thus, a pattern guidance displaydepicting event patterns detected during operation of the infusiondevice 102 may depict an event pattern that is influenced by thedetected meal and includes a graphical indication of the detected mealassociated with a time of day corresponding to the analysis interval, asdescribed in greater detail below in the context of FIG. 7. In yet otherembodiments, the meal indication may be utilized to temporarily alter orinfluence operation of the infusion device 102 (e.g., to modify insulindelivery to account for the meal and/or bolus) as appropriate.

It should be noted that in general, an individual's blood sugar levelsare generally influenced by food, exercise, medicine, sleep, andphysiological stress (e.g., illness, mental stress, or the like).Accordingly, by virtue of the meal detection process 600 detecting ameal that could otherwise go unannounced or without indication by auser, other pattern detection algorithms can use this additionalknowledge of meal consumption to better understand the patient'scondition to provide recommendations or guidance for post-meal control,such as, for example, recommended medication regimen, eating habits ornutritional guidance, recommended proper medication types, or otherbehavioral profiling based on eating habits.

It should be noted that the meal detection process 600 may beincrementally performed on sensor glucose measurement values to classifyor otherwise categorize as many non-overlapping segments of the patienthistory as meal or non-meal segments for use in analyzing the patient'smeal response, validating the meal detection model, or the like. In thisregard, when a meal bolus is manually-indicated or announced, the mealdetection process 600 may still be performed to verify or otherwiseconfirm that the meal detection model generates an output indicative ofa meal. For example, in one or more embodiment, the server 106 maydynamically update or track the performance of the model over time anddetect when one or more performance metrics associated with the modelfail to satisfy the validation criteria (e.g., task 218). In thisregard, when the model performance suffers, the patient modeling process200 may be repeated to update the patient's meal detection model orreassign the patient to a population model as described above.

FIG. 7 depicts an exemplary embodiment of a snapshot GUI display 700 orreport that may be presented on a display device associated with anelectronic device 102, 106, 110 in the patient management system 100 inconjunction with the meal detection process 600 described above. Thesnapshot GUI display 700 includes a plurality of regions 702, 704, 706,708 that present information pertaining to past operation of theinfusion device 102 to deliver insulin to regulate the glucose level ofa diabetic patient. A header region 702 is presented at the top of thesnapshot GUI display 700 and includes a graphical representation of apreceding time period of operation (e.g., November 3-November 6)associated with the snapshot GUI display 700 for which information ispresented in the below regions 704, 706, 708. A performance metricregion 704 is presented below the header region 702 and includesgraphical representations or other indicia of the values for variousperformance metrics calculated based on the historical measurement datafor the patient's glucose level over the time period associated with thesnapshot GUI display 700, such as, for example, an average sensorglucose measurement value, an estimated A1C level, estimated percentagesof the snapshot time period during which durations the sensor glucosemeasurement values were above, below or between one or more glucosethreshold values, and the like.

The illustrated snapshot GUI display 700 includes a graph overlay region708 presented at the bottom of the snapshot GUI display 700 thatincludes graphical representations of historical measurement data forthe patient's glucose level over the snapshot time period with respectto time. In this regard, the graph overlay region 708 may include a linegraph including a line associated with each day within the snapshot timeperiod that depicts the patient's sensor glucose measurements valuesfrom that day with respect to time of day. Additionally, the graphoverlay region 708 may include a line representative of the average ofthe patient's sensor glucose measurements across the different dayswithin the snapshot time period with respect to time of day. Theillustrated graph overlay region 708 also includes a visuallydistinguishable overlay region that indicates a target range for thepatient's sensor glucose measurement values. In exemplary embodiments,the graphical representation of the measurements for each different dayor date depicted on the graph overlay region 708 is rendered with aunique color or other visually distinguishable characteristic relativeto the graphical representations corresponding to other days or dates,with the meal markers 750 on that respective day or date also beingrendered in the same color or visually distinguishable characteristicand placed on the line corresponding to that respective day or date.

Referring now to FIGS. 6-7, in accordance with one or more embodiments,one or more of the meal markers 750 are generated in response to themeal detection process 600 detecting a meal segment within thehistorical measurement data for the patient's glucose level over thesnapshot time period. For example, the meal detection process 600 maydetect a meal segment corresponding to the period of 12:30 PM-1:30 PM ofthe patient's measurement data for November 5^(th), and in response, themeal detection process 600 may store or otherwise provide an indicationof a meal associated with the sensor glucose measurement valuecorresponding to the start of that meal segment the absence of a mealindication within the patient's bolus history. Thus, even if the patientdoes not manually provide the meal indication, a corresponding mealmarker 750 for that meal may still be presented on the snapshot GUIdisplay 700.

The illustrated snapshot GUI display 700 also includes a patterndetection region 706 that includes a plurality of pattern guidancedisplays 720, 730, 740, where each pattern guidance display 720, 730,740 corresponds to a respective pattern of events identified during thesnapshot time period based on the patient's sensor glucose measurementvalues for the snapshot time period. In this regard, the historicalsensor glucose measurement values are analyzed for different monitoringperiods within the snapshot time period. Here, it is noted that the mealindications generated by the meal detection process 600 may be utilizedto delineate or otherwise determine the times or portions of the daycorresponding to monitoring periods referenced to meals. For example,the meal marker 750 identified based on the period of 12:30 PM-1:30 PMof the patient's measurement data for November 5^(th) may be utilized todetermine one or more of a starting time, an ending time, or a durationof a lunch time monitoring period in a manner that ensures the lunchtime monitoring period includes or otherwise encompasses the mealdetected by the meal detection process 600. Thus, patterns for meal timemonitoring period may be identified and analyzed even if the patientdoes not manually provide a meal indication for that particular meal.For example, a patient may routinely or habitually fail to manuallyindicate when he or she has consumed lunch due to the patient beingpreoccupied with other daily activities. By virtue of thepatient-specific meal detection described above, lunch time eventpatterns for that patient may be better identified by tailoring thelunch time monitoring period to fit the patient's midday meal times.Thus, the duration of the lunch time monitoring period depicted by thelunch time monitoring period marker 728 described below encompasses themeal marker 750 detected by the meal detection process 600.

Based on the subset of sensor glucose measurement values associated withtimes of day within a respective monitoring period, a pattern of one ormore events is detected or otherwise identified within that monitoringperiod, such as, for example, a glucose variability event, a highglucose (or hyperglycemic) event, or a low glucose (or hypoglycemic)event. When a plurality of event patterns are identified, the identifiedevent patterns are prioritized and filtered to limit the number of eventpatterns for display. For example, in one embodiment, the detected eventpatterns are prioritized primarily based on event type (e.g., from mostsignificant to least significant) and secondarily based on themonitoring period associated with the respective event pattern, and thenfiltered to remove lower priority event patterns above a displaythreshold that limits the number of displayed event patterns.

For each remaining event pattern of the filtered prioritized list, apattern guidance display 720, 730, 740 may be generated that includesgraphical indicia of the event type and the monitoring period associatedwith the detected pattern, graphical indicia of the number, frequency,severity, or other characteristics of the events associated with thedetected pattern, graphical indicia of potential causes or remedialactions for the detected events, and the like, which may also beprioritized or ordered according to their respective clinical relevance.Additionally, in exemplary embodiments, graphical indicia 728, 738, 748of the detected event patterns are presented within the graph overlyregion 708 in a manner that establishes an association between thedetected event pattern, the time of day associated with itscorresponding monitoring period, and its relative priority level. Thus,the graphical indicia 728, 738, 748 facilitate establishing anassociation between a respective subset of the historical measurementdata presented within the graph overlay region 708 and a correspondingevent pattern detected based on that subset of historical measurementdata. For example, in the illustrated embodiment of FIG. 7, a marker 728is presented overlying the graph overlay region 708 that includes anidentifier that indicates the detected event pattern the marker 728corresponds to (e.g., number 1 to indicate the highest priority eventpattern 720), and the marker 728 has a width or other dimension thatencompasses or otherwise corresponds to the subset of the sensor glucosemeasurement values associated with the time of day corresponding to themonitoring period associated with the detected event pattern (e.g., thelunch time period).

FIG. 8 illustrates a computing device 800 including a display 833suitable for presenting a snapshot GUI display 700 as part of a diabetesdata management system in conjunction with the processes 200, 600 ofFIGS. 2 and 6 described above. The diabetes data management system(DDMS) may be referred to as the Medtronic MiniMed CARELINK™ system oras a medical data management system (MDMS) in some embodiments. The DDMSmay be housed on a server or a plurality of servers which a user or ahealth care professional may access via a communications network via theInternet or the World Wide Web. Some models of the DDMS, which isdescribed as an MDMS, are described in U.S. Patent ApplicationPublication Nos. 2006/0031094 and 2013/0338630, which is hereinincorporated by reference in their entirety.

While description of embodiments are made in regard to monitoringmedical or biological conditions for subjects having diabetes, thesystems and processes herein are applicable to monitoring medical orbiological conditions for cardiac subjects, cancer subjects, HIVsubjects, subjects with other disease, infection, or controllableconditions, or various combinations thereof.

In various embodiments, the DDMS may be installed in a computing devicein a health care provider's office, such as a doctor's office, a nurse'soffice, a clinic, an emergency room, an urgent care office. Health careproviders may be reluctant to utilize a system where their confidentialpatient data is to be stored in a computing device such as a server onthe Internet.

The DDMS may be installed on a computing device 800. The computingdevice 800 may be coupled to a display 833. In some embodiments, thecomputing device 800 may be in a physical device separate from thedisplay (such as in a personal computer, a mini-computer, etc.) In someembodiments, the computing device 800 may be in a single physicalenclosure or device with the display 833 such as a laptop where thedisplay 833 is integrated into the computing device. In variousembodiments, the computing device 800 hosting the DDMS may be, but isnot limited to, a desktop computer, a laptop computer, a server, anetwork computer, a personal digital assistant (PDA), a portabletelephone including computer functions, a pager with a large visibledisplay, an insulin pump including a display, a glucose sensor includinga display, a glucose meter including a display, and/or a combinationinsulin pump/glucose sensor having a display. The computing device mayalso be an insulin pump coupled to a display, a glucose meter coupled toa display, or a glucose sensor coupled to a display. The computingdevice 800 may also be a server located on the Internet that isaccessible via a browser installed on a laptop computer, desktopcomputer, a network computer, or a PDA. The computing device 800 mayalso be a server located in a doctor's office that is accessible via abrowser installed on a portable computing device, e.g., laptop, PDA,network computer, portable phone, which has wireless capabilities andcan communicate via one of the wireless communication protocols such asBluetooth and IEEE 802.11 protocols.

In the embodiment shown in FIG. 8, the data management system 816comprises a group of interrelated software modules or layers thatspecialize in different tasks. The system software includes a devicecommunication layer 824, a data parsing layer 826, a database layer 828,database storage devices 829, a reporting layer 830, a graph displaylayer 831, and a user interface layer 832. The diabetes data managementsystem may communicate with a plurality of subject support devices 812,two of which are illustrated in FIG. 8. Although the different referencenumerals refer to a number of layers, (e.g., a device communicationlayer, a data parsing layer, a database layer), each layer may include asingle software module or a plurality of software modules. For example,the device communications layer 824 may include a number of interactingsoftware modules, libraries, etc. In some embodiments, the datamanagement system 816 may be installed onto a non-volatile storage area(memory such as flash memory, hard disk, removable hard, DVD-RW, CD-RW)of the computing device 800. If the data management system 816 isselected or initiated, the system 816 may be loaded into a volatilestorage (memory such as DRAM, SRAM, RAM, DDRAM) for execution.

The device communication layer 824 is responsible for interfacing withat least one, and, in further embodiments, to a plurality of differenttypes of subject support devices 812, such as, for example, bloodglucose meters, glucose sensors/monitors, or an infusion pump. In oneembodiment, the device communication layer 824 may be configured tocommunicate with a single type of subject support device 812. However,in more comprehensive embodiments, the device communication layer 824 isconfigured to communicate with multiple different types of subjectsupport devices 812, such as devices made from multiple differentmanufacturers, multiple different models from a particular manufacturerand/or multiple different devices that provide different functions (suchas infusion functions, sensing functions, metering functions,communication functions, user interface functions, or combinationsthereof). By providing an ability to interface with multiple differenttypes of subject support devices 812, the diabetes data managementsystem 816 may collect data from a significantly greater number ofdiscrete sources. Such embodiments may provide expanded and improveddata analysis capabilities by including a greater number of subjects andgroups of subjects in statistical or other forms of analysis that canbenefit from larger amounts of sample data and/or greater diversity insample data, and, thereby, improve capabilities of determiningappropriate treatment parameters, diagnostics, or the like.

The device communication layer 824 allows the DDMS 816 to receiveinformation from and transmit information to or from each subjectsupport device 812 in the system 816. Depending upon the embodiment andcontext of use, the type of information that may be communicated betweenthe system 816 and device 812 may include, but is not limited to, data,programs, updated software, education materials, warning messages,notifications, device settings, therapy parameters, or the like. Thedevice communication layer 824 may include suitable routines fordetecting the type of subject support device 812 in communication withthe system 816 and implementing appropriate communication protocols forthat type of device 812. Alternatively or in addition, the subjectsupport device 812 may communicate information in packets or other dataarrangements, where the communication includes a preamble or otherportion that includes device identification information for identifyingthe type of the subject support device. Alternatively, or in addition,the subject support device 812 may include suitable user-operableinterfaces for allowing a user to enter information, such as byselecting an optional icon or text or other device identifier, thatcorresponds to the type of subject support device used by that user.Such information may be communicated to the system 816, through anetwork connection. In yet further embodiments, the system 816 maydetect the type of subject support device 812 it is communicating withand then may send a message requiring the user to verify that the system816 properly detected the type of subject support device being used bythe user. For systems 816 that are capable of communicating withmultiple different types of subject support devices 812, the devicecommunication layer 824 may be capable of implementing multipledifferent communication protocols and selects a protocol that isappropriate for the detected type of subject support device.

The data-parsing layer 826 is responsible for validating the integrityof device data received and for inputting it correctly into a database829. A cyclic redundancy check CRC process for checking the integrity ofthe received data may be employed. Alternatively, or in addition, datamay be received in packets or other data arrangements, where preamblesor other portions of the data include device type identificationinformation. Such preambles or other portions of the received data mayfurther include device serial numbers or other identificationinformation that may be used for validating the authenticity of thereceived information. In such embodiments, the system 816 may comparereceived identification information with pre-stored information toevaluate whether the received information is from a valid source.

The database layer 828 may include a centralized database repositorythat is responsible for warehousing and archiving stored data in anorganized format for later access, and retrieval. The database layer 828operates with one or more data storage device(s) 829 suitable forstoring and providing access to data in the manner described herein.Such data storage device(s) 829 may comprise, for example, one or morehard discs, optical discs, tapes, digital libraries or other suitabledigital or analog storage media and associated drive devices, drivearrays or the like.

Data may be stored and archived for various purposes, depending upon theembodiment and environment of use. Information regarding specificsubjects and patient support devices may be stored and archived and madeavailable to those specific subjects, their authorized healthcareproviders and/or authorized healthcare payor entities for analyzing thesubject's condition. Also, certain information regarding groups ofsubjects or groups of subject support devices may be made available moregenerally for healthcare providers, subjects, personnel of the entityadministering the system 816 or other entities, for analyzing group dataor other forms of conglomerate data.

Embodiments of the database layer 828 and other components of the system816 may employ suitable data security measures for securing personalmedical information of subjects, while also allowing non-personalmedical information to be more generally available for analysis.Embodiments may be configured for compliance with suitable governmentregulations, industry standards, policies or the like, including, butnot limited to the Health Insurance Portability and Accountability Actof 1996 (HIPAA).

The database layer 828 may be configured to limit access of each user totypes of information pre-authorized for that user. For example, asubject may be allowed access to his or her individual medicalinformation (with individual identifiers) stored by the database layer828, but not allowed access to other subject's individual medicalinformation (with individual identifiers). Similarly, a subject'sauthorized healthcare provider or payor entity may be provided access tosome or all of the subject's individual medical information (withindividual identifiers) stored by the database layer 828, but notallowed access to another individual's personal information. Also, anoperator or administrator-user (on a separate computer communicatingwith the computing device 800) may be provided access to some or allsubject information, depending upon the role of the operator oradministrator. On the other hand, a subject, healthcare provider,operator, administrator or other entity, may be authorized to accessgeneral information of unidentified individuals, groups or conglomerates(without individual identifiers) stored by the database layer 828 in thedata storage devices 829.

In embodiments of the subject matter described herein, the databaselayer 828 may store patient-specific event detection models, populationgroup detection models, and individual patient preference profiles. Inthe database layer 828, for example, each user may store informationregarding specific parameters that correspond to the user.Illustratively, these parameters could include target blood glucose orsensor glucose levels, what type of equipment the users utilize (insulinpump, glucose sensor, blood glucose meter, etc.) and could be stored ina record, a file, or a memory location in the data storage device(s) 829in the database layer. The preference profiles may include variousthreshold values, monitoring period values, prioritization criteria,filtering criteria, and/or other user-specific values for parametersutilized to generate a snapshot GUI display, such as snapshot GUIdisplay 700, on the display 833 or a support device 812 in apersonalized or patient-specific manner. Additionally, data orinformation defining the meal detection or other event detection modelsassociated with a particular individual may also be stored in a record,a file, or a memory location associated with that patient in the datastorage device(s) 829 in the database layer.

The DDMS 816 may measure, analyze, and track either blood glucose (BG)or sensor glucose (SG) readings for a user. In exemplary embodiments,the medical data management system may measure, track, or analyze bothBG and SG readings for the user. Accordingly, although certain reportsmay mention or illustrate BG or SG only, the reports may monitor anddisplay results for the other one of the glucose readings or for both ofthe glucose readings.

The reporting layer 830 may include a report wizard program that pullsdata from selected locations in the database 829 and generates reportinformation from the desired parameters of interest. The reporting layer830 may be configured to generate multiple different types of reports,each having different information and/or showing information indifferent formats (arrangements or styles), where the type of report maybe selectable by the user. A plurality of pre-set types of report (withpre-defined types of content and format) may be available and selectableby a user. At least some of the pre-set types of reports may be common,industry standard report types with which many healthcare providersshould be familiar. In exemplary embodiments described herein, thereporting layer 830 also facilitates generation of a snapshot reportincluding a snapshot GUI display, such as snapshot GUI display 700 ofFIG. 7.

In some embodiments, the database layer 828 may calculate values forvarious medical information that is to be displayed on the reportsgenerated by the report or reporting layer 830. For example, thedatabase layer 828, may calculate average blood glucose or sensorglucose readings for specified timeframes. In some embodiments, thereporting layer 830 may calculate values for medical or physicalinformation that is to be displayed on the reports. For example, a usermay select parameters which are then utilized by the reporting layer 830to generate medical information values corresponding to the selectedparameters. In other embodiments, the user may select a parameterprofile that previously existed in the database layer 828.

Alternatively, or in addition, the report wizard may allow a user todesign a custom type of report. For example, the report wizard may allowa user to define and input parameters (such as parameters specifying thetype of content data, the time period of such data, the format of thereport, or the like) and may select data from the database and arrangethe data in a printable or displayable arrangement, based on theuser-defined parameters. In further embodiments, the report wizard mayinterface with or provide data for use by other programs that may beavailable to users, such as common report generating, formatting orstatistical analysis programs. In this manner, users may import datafrom the system 816 into further reporting tools familiar to the user.The reporting layer 830 may generate reports in displayable form toallow a user to view reports on a standard display device, printableform to allow a user to print reports on standard printers, or othersuitable forms for access by a user. Embodiments may operate withconventional file format schemes for simplifying storing, printing andtransmitting functions, including, but not limited to PDF, JPEG, or thelike. Illustratively, a user may select a type of report and parametersfor the report and the reporting layer 830 may create the report in aPDF format. A PDF plug-in may be initiated to help create the report andalso to allow the user to view the report. Under these operatingconditions, the user may print the report utilizing the PDF plug-in. Incertain embodiments in which security measures are implemented, forexample, to meet government regulations, industry standards or policiesthat restrict communication of subject's personal information, some orall reports may be generated in a form (or with suitable softwarecontrols) to inhibit printing, or electronic transfer (such as anon-printable and/or non-capable format). In yet further embodiments,the system 816 may allow a user generating a report to designate thereport as non-printable and/or non-transferable, whereby the system 816will provide the report in a form that inhibits printing and/orelectronic transfer.

The reporting layer 830 may transfer selected reports to the graphdisplay layer 831. The graph display layer 831 receives informationregarding the selected reports and converts the data into a format thatcan be displayed or shown on a display 833.

In various embodiments, the reporting layer 830 may store a number ofthe user's parameters. Illustratively, the reporting layer 830 may storethe type of carbohydrate units, a blood glucose movement or sensorglucose reading, a carbohydrate conversion factor, and timeframes forspecific types of reports. These examples are meant to be illustrativeand not limiting.

Data analysis and presentations of the reported information may beemployed to develop and support diagnostic and therapeutic parameters.Where information on the report relates to an individual subject, thediagnostic and therapeutic parameters may be used to assess the healthstatus and relative well-being of that subject, assess the subject'scompliance to a therapy, as well as to develop or modify treatment forthe subject and assess the subject's behaviors that affect his/hertherapy. Where information on the report relates to groups of subjectsor conglomerates of data, the diagnostic and therapeutic parameters maybe used to assess the health status and relative well-being of groups ofsubjects with similar medical conditions, such as, but not limited to,diabetic subjects, cardiac subjects, diabetic subjects having aparticular type of diabetes or cardiac condition, subjects of aparticular age, sex or other demographic group, subjects with conditionsthat influence therapeutic decisions such as but not limited topregnancy, obesity, hypoglycemic unawareness, learning disorders,limited ability to care for self, various levels of insulin resistance,combinations thereof, or the like.

The user interface layer 832 supports interactions with the end user,for example, for user login and data access, software navigation, datainput, user selection of desired report types and the display ofselected information. Users may also input parameters to be utilized inthe selected reports via the user interface layer 832. Examples of usersinclude but are not limited to: healthcare providers, healthcare payerentities, system operators or administrators, researchers, businessentities, healthcare institutions and organizations, or the like,depending upon the service being provided by the system and dependingupon the embodiment. More comprehensive embodiments are capable ofinteracting with some or all of the above-noted types of users, whereindifferent types of users have access to different services or data ordifferent levels of services or data.

In an example embodiment, the user interface layer 832 provides one ormore websites accessible by users on the Internet. The user interfacelayer may include or operate with at least one (or multiple) suitablenetwork server(s) to provide the website(s) over the Internet and toallow access, world-wide, from Internet-connected computers usingstandard Internet browser software. The website(s) may be accessed byvarious types of users, including but not limited to subjects,healthcare providers, researchers, business entities, healthcareinstitutions and organizations, payor entities, pharmaceutical partnersor other sources of pharmaceuticals or medical equipment, and/or supportpersonnel or other personnel running the system 816, depending upon theembodiment of use.

In another example embodiment, where the DDMS 816 is located on onecomputing device 800, the user interface layer 832 provides a number ofmenus to the user to navigate through the DDMS. These menus may becreated utilizing any menu format, including but not limited to HTML,XML, or Active Server pages. A user may access the DDMS 816 to performone or more of a variety of tasks, such as accessing general informationmade available on a website to all subjects or groups of subjects. Theuser interface layer 832 of the DDMS 816 may allow a user to accessspecific information or to generate reports regarding that subject'smedical condition or that subject's medical device(s) 812, to transferdata or other information from that subject's support device(s) 812 tothe system 816, to transfer data, programs, program updates or otherinformation from the system 816 to the subject's support device(s) 812,to manually enter information into the system 816, to engage in a remoteconsultation exchange with a healthcare provider, or to modify thecustom settings in a subject's supported device and/or in a subject'sDDMS/MDMS data file.

The system 816 may provide access to different optional resources oractivities (including accessing different information items andservices) to different users and to different types or groups of users,such that each user may have a customized experience and/or each type orgroup of user (e.g., all users, diabetic users, cardio users, healthcareprovider-user or payor-user, or the like) may have a different set ofinformation items or services available on the system. The system 816may include or employ one or more suitable resource provisioning programor system for allocating appropriate resources to each user or type ofuser, based on a pre-defined authorization plan. Resource provisioningsystems are well known in connection with provisioning of electronicoffice resources (email, software programs under license, sensitivedata, etc.) in an office environment, for example, in a local areanetwork LAN for an office, company or firm. In one example embodiment,such resource provisioning systems is adapted to control access tomedical information and services on the DDMS 816, based on the type ofuser and/or the identity of the user.

Upon entering successful verification of the user's identificationinformation and password, the user may be provided access to secure,personalized information stored on the DDMS 816. For example, the usermay be provided access to a secure, personalized location in the DDMS816 which has been assigned to the subject. This personalized locationmay be referred to as a personalized screen, a home screen, a home menu,a personalized page, etc. The personalized location may provide apersonalized home screen to the subject, including selectable icons ormenu items for selecting optional activities, including, for example, anoption to transfer device data from a subject's supported device 812 tothe system 816, manually enter additional data into the system 816,modify the subject's custom settings, and/or view and print reports.Reports may include data specific to the subject's condition, includingbut not limited to, data obtained from the subject's subject supportdevice(s) 812, data manually entered, data from medical libraries orother networked therapy management systems, data from the subjects orgroups of subjects, or the like. Where the reports includesubject-specific information and subject identification information, thereports may be generated from some or all subject data stored in asecure storage area (e.g., storage devices 829) employed by the databaselayer 828.

The user may select an option to transfer (send) device data to themedical data management system 816. If the system 816 receives a user'srequest to transfer device data to the system, the system 816 mayprovide the user with step-by-step instructions on how to transfer datafrom the subject's supported device(s) 812. For example, the DDMS 816may have a plurality of different stored instruction sets forinstructing users how to download data from different types of subjectsupport devices, where each instruction set relates to a particular typeof subject supported device (e.g., pump, sensor, meter, or the like), aparticular manufacturer's version of a type of subject support device,or the like. Registration information received from the user duringregistration may include information regarding the type of subjectsupport device(s) 812 used by the subject. The system 816 employs thatinformation to select the stored instruction set(s) associated with theparticular subject's support device(s) 812 for display to the user.

Other activities or resources available to the user on the system 816may include an option for manually entering information to the DDMS/MDMS816. For example, from the user's personalized menu or location, theuser may select an option to manually enter additional information intothe system 816.

Further optional activities or resources may be available to the user onthe DDMS 816. For example, from the user's personalized menu, the usermay select an option to receive data, software, software updates,treatment recommendations or other information from the system 816 onthe subject's support device(s) 812. If the system 816 receives arequest from a user to receive data, software, software updates,treatment recommendations or other information, the system 816 mayprovide the user with a list or other arrangement of multiple selectableicons or other indicia representing available data, software, softwareupdates or other information available to the user.

Yet further optional activities or resources may be available to theuser on the medical data management system 816 including, for example,an option for the user to customize or otherwise further personalize theuser's personalized location or menu. In particular, from the user'spersonalized location, the user may select an option to customizeparameters for the user. In addition, the user may create profiles ofcustomizable parameters. When the system 816 receives such a requestfrom a user, the system 816 may provide the user with a list or otherarrangement of multiple selectable icons or other indicia representingparameters that may be modified to accommodate the user's preferences.When a user selects one or more of the icons or other indicia, thesystem 816 may receive the user's request and makes the requestedmodification.

FIG. 9 depicts one exemplary embodiment of an infusion system 900 thatincludes, without limitation, a fluid infusion device (or infusion pump)902, a sensing arrangement 904, a command control device (CCD) 906, anda computer 908, which could be realized as any one of the computingdevices 106, 110, 800, 812 described above. The components of aninfusion system 900 may be realized using different platforms, designs,and configurations, and the embodiment shown in FIG. 9 is not exhaustiveor limiting. In practice, the infusion device 902 and the sensingarrangement 904 are secured at desired locations on the body of a user(or patient), as illustrated in FIG. 9. In this regard, the locations atwhich the infusion device 902 and the sensing arrangement 904 aresecured to the body of the user in FIG. 9 are provided only as arepresentative, non-limiting, example. The elements of the infusionsystem 900 may be similar to those described in U.S. Pat. No. 8,674,288,the subject matter of which is hereby incorporated by reference in itsentirety.

In the illustrated embodiment of FIG. 9, the infusion device 902 isdesigned as a portable medical device suitable for infusing a fluid, aliquid, a gel, or other agent into the body of a user. In exemplaryembodiments, the infused fluid is insulin, although many other fluidsmay be administered through infusion such as, but not limited to, HIVdrugs, drugs to treat pulmonary hypertension, iron chelation drugs, painmedications, anti-cancer treatments, medications, vitamins, hormones, orthe like. In some embodiments, the fluid may include a nutritionalsupplement, a dye, a tracing medium, a saline medium, a hydrationmedium, or the like.

The sensing arrangement 904 generally represents the components of theinfusion system 900 configured to sense, detect, measure or otherwisequantify a condition of the user, and may include a sensor, a monitor,or the like, for providing data indicative of the condition that issensed, detected, measured or otherwise monitored by the sensingarrangement. In this regard, the sensing arrangement 904 may includeelectronics and enzymes reactive to a biological or physiologicalcondition of the user, such as a blood glucose level, or the like, andprovide data indicative of the blood glucose level to the infusiondevice 902, the CCD 906 and/or the computer 908. For example, theinfusion device 902, the CCD 906 and/or the computer 908 may include adisplay for presenting information or data to the user based on thesensor data received from the sensing arrangement 904, such as, forexample, a current glucose level of the user, a graph or chart of theuser's glucose level versus time, device status indicators, alertmessages, or the like. In other embodiments, the infusion device 902,the CCD 906 and/or the computer 908 may include electronics and softwarethat are configured to analyze sensor data and operate the infusiondevice 902 to deliver fluid to the body of the user based on the sensordata and/or preprogrammed delivery routines. Thus, in exemplaryembodiments, one or more of the infusion device 902, the sensingarrangement 904, the CCD 906, and/or the computer 908 includes atransmitter, a receiver, and/or other transceiver electronics that allowfor communication with other components of the infusion system 900, sothat the sensing arrangement 904 may transmit sensor data or monitordata to one or more of the infusion device 902, the CCD 906 and/or thecomputer 908.

Still referring to FIG. 9, in various embodiments, the sensingarrangement 904 may be secured to the body of the user or embedded inthe body of the user at a location that is remote from the location atwhich the infusion device 902 is secured to the body of the user. Invarious other embodiments, the sensing arrangement 904 may beincorporated within the infusion device 902. In other embodiments, thesensing arrangement 904 may be separate and apart from the infusiondevice 902, and may be, for example, part of the CCD 906. In suchembodiments, the sensing arrangement 904 may be configured to receive abiological sample, analyte, or the like, to measure a condition of theuser.

In various embodiments, the CCD 906 and/or the computer 908 may includeelectronics and other components configured to perform processing,delivery routine storage, and to control the infusion device 902 in amanner that is influenced by sensor data measured by and/or receivedfrom the sensing arrangement 904. By including control functions in theCCD 906 and/or the computer 908, the infusion device 902 may be madewith more simplified electronics. However, in other embodiments, theinfusion device 902 may include all control functions, and may operatewithout the CCD 906 and/or the computer 908. In various embodiments, theCCD 906 may be a portable electronic device. In addition, in variousembodiments, the infusion device 902 and/or the sensing arrangement 904may be configured to transmit data to the CCD 906 and/or the computer908 for display or processing of the data by the CCD 906 and/or thecomputer 908.

In some embodiments, the CCD 906 and/or the computer 908 may provideinformation to the user that facilitates the user's subsequent use ofthe infusion device 902. For example, the CCD 906 may provideinformation to the user to allow the user to determine the rate or doseof medication to be administered into the user's body. In otherembodiments, the CCD 906 may provide information to the infusion device902 to autonomously control the rate or dose of medication administeredinto the body of the user. In some embodiments, the sensing arrangement904 may be integrated into the CCD 906. Such embodiments may allow theuser to monitor a condition by providing, for example, a sample of hisor her blood to the sensing arrangement 904 to assess his or hercondition. In some embodiments, the sensing arrangement 904 and the CCD906 may be used for determining glucose levels in the blood and/or bodyfluids of the user without the use of, or necessity of, a wire or cableconnection between the infusion device 902 and the sensing arrangement904 and/or the CCD 906.

In one or more exemplary embodiments, the sensing arrangement 904 and/orthe infusion device 902 are cooperatively configured to utilize aclosed-loop system for delivering fluid to the user. Examples of sensingdevices and/or infusion pumps utilizing closed-loop systems may be foundat, but are not limited to, the following U.S. Pat. Nos. 6,088,608,6,119,028, 6,589,229, 6,740,072, 6,827,702, 7,323,142, and 7,402,153,all of which are incorporated herein by reference in their entirety. Insuch embodiments, the sensing arrangement 904 is configured to sense ormeasure a condition of the user, such as, blood glucose level or thelike. The infusion device 902 is configured to deliver fluid in responseto the condition sensed by the sensing arrangement 904. In turn, thesensing arrangement 904 continues to sense or otherwise quantify acurrent condition of the user, thereby allowing the infusion device 902to deliver fluid continuously in response to the condition currently (ormost recently) sensed by the sensing arrangement 904 indefinitely. Insome embodiments, the sensing arrangement 904 and/or the infusion device902 may be configured to utilize the closed-loop system only for aportion of the day, for example only when the user is asleep or awake.

FIGS. 10-12 depict one exemplary embodiment of a fluid infusion device1000 (or alternatively, infusion pump) suitable for use in an infusionsystem, such as, for example, as infusion device 902 in the infusionsystem 900 of FIG. 9 or as infusion device 102 in the patient managementsystem 100 of FIG. 1. The fluid infusion device 1000 is a portablemedical device designed to be carried or worn by a patient (or user),and the fluid infusion device 1000 may leverage any number ofconventional features, components, elements, and characteristics ofexisting fluid infusion devices, such as, for example, some of thefeatures, components, elements, and/or characteristics described in U.S.Pat. Nos. 6,485,465 and 7,621,893. It should be appreciated that FIGS.10-12 depict some aspects of the infusion device 1000 in a simplifiedmanner; in practice, the infusion device 1000 could include additionalelements, features, or components that are not shown or described indetail herein.

As best illustrated in FIGS. 10-11, the illustrated embodiment of thefluid infusion device 1000 includes a housing 1002 adapted to receive afluid-containing reservoir 1005. An opening 1020 in the housing 1002accommodates a fitting 1023 (or cap) for the reservoir 1005, with thefitting 1023 being configured to mate or otherwise interface with tubing1021 of an infusion set 1025 that provides a fluid path to/from the bodyof the user. In this manner, fluid communication from the interior ofthe reservoir 1005 to the user is established via the tubing 1021. Theillustrated fluid infusion device 1000 includes a human-machineinterface (HMI) 1030 (or user interface) that includes elements 1032,1034 that can be manipulated by the user to administer a bolus of fluid(e.g., insulin), to change therapy settings, to change user preferences,to select display features, and the like. The infusion device alsoincludes a display element 1026, such as a liquid crystal display (LCD)or another suitable display element, that can be used to present varioustypes of information or data to the user, such as, without limitation:the current glucose level of the patient; the time; a graph or chart ofthe patient's glucose level versus time; device status indicators; etc.

The housing 1002 is formed from a substantially rigid material having ahollow interior 1014 adapted to allow an electronics assembly 1004, asliding member (or slide) 1006, a drive system 1008, a sensor assembly1010, and a drive system capping member 1012 to be disposed therein inaddition to the reservoir 1005, with the contents of the housing 1002being enclosed by a housing capping member 1016. The opening 1020, theslide 1006, and the drive system 1008 are coaxially aligned in an axialdirection (indicated by arrow 1018), whereby the drive system 1008facilitates linear displacement of the slide 1006 in the axial direction1018 to dispense fluid from the reservoir 1005 (after the reservoir 1005has been inserted into opening 1020), with the sensor assembly 1010being configured to measure axial forces (e.g., forces aligned with theaxial direction 1018) exerted on the sensor assembly 1010 responsive tooperating the drive system 1008 to displace the slide 1006. In variousembodiments, the sensor assembly 1010 may be utilized to detect one ormore of the following: an occlusion in a fluid path that slows,prevents, or otherwise degrades fluid delivery from the reservoir 1005to a user's body; when the reservoir 1005 is empty; when the slide 1006is properly seated with the reservoir 1005; when a fluid dose has beendelivered; when the infusion pump 1000 is subjected to shock orvibration; when the infusion pump 1000 requires maintenance.

Depending on the embodiment, the fluid-containing reservoir 1005 may berealized as a syringe, a vial, a cartridge, a bag, or the like. Incertain embodiments, the infused fluid is insulin, although many otherfluids may be administered through infusion such as, but not limited to,HIV drugs, drugs to treat pulmonary hypertension, iron chelation drugs,pain medications, anti-cancer treatments, medications, vitamins,hormones, or the like. As best illustrated in FIGS. 11-12, the reservoir1005 typically includes a reservoir barrel 1019 that contains the fluidand is concentrically and/or coaxially aligned with the slide 1006(e.g., in the axial direction 1018) when the reservoir 1005 is insertedinto the infusion pump 1000. The end of the reservoir 1005 proximate theopening 1020 may include or otherwise mate with the fitting 1023, whichsecures the reservoir 1005 in the housing 1002 and prevents displacementof the reservoir 1005 in the axial direction 1018 with respect to thehousing 1002 after the reservoir 1005 is inserted into the housing 1002.As described above, the fitting 1023 extends from (or through) theopening 1020 of the housing 1002 and mates with tubing 1021 to establishfluid communication from the interior of the reservoir 1005 (e.g.,reservoir barrel 1019) to the user via the tubing 1021 and infusion set1025. The opposing end of the reservoir 1005 proximate the slide 1006includes a plunger 1017 (or stopper) positioned to push fluid frominside the barrel 1019 of the reservoir 1005 along a fluid path throughtubing 1021 to a user. The slide 1006 is configured to mechanicallycouple or otherwise engage with the plunger 1017, thereby becomingseated with the plunger 1017 and/or reservoir 1005. Fluid is forced fromthe reservoir 1005 via tubing 1021 as the drive system 1008 is operatedto displace the slide 1006 in the axial direction 1018 toward theopening 1020 in the housing 1002.

In the illustrated embodiment of FIGS. 11-12, the drive system 1008includes a motor assembly 1007 and a drive screw 1009. The motorassembly 1007 includes a motor that is coupled to drive train componentsof the drive system 1008 that are configured to convert rotational motormotion to a translational displacement of the slide 1006 in the axialdirection 1018, and thereby engaging and displacing the plunger 1017 ofthe reservoir 1005 in the axial direction 1018. In some embodiments, themotor assembly 1007 may also be powered to translate the slide 1006 inthe opposing direction (e.g., the direction opposite direction 1018) toretract and/or detach from the reservoir 1005 to allow the reservoir1005 to be replaced. In exemplary embodiments, the motor assembly 1007includes a brushless DC (BLDC) motor having one or more permanentmagnets mounted, affixed, or otherwise disposed on its rotor. However,the subject matter described herein is not necessarily limited to usewith BLDC motors, and in alternative embodiments, the motor may berealized as a solenoid motor, an AC motor, a stepper motor, apiezoelectric caterpillar drive, a shape memory actuator drive, anelectrochemical gas cell, a thermally driven gas cell, a bimetallicactuator, or the like. The drive train components may comprise one ormore lead screws, cams, ratchets, jacks, pulleys, pawls, clamps, gears,nuts, slides, bearings, levers, beams, stoppers, plungers, sliders,brackets, guides, bearings, supports, bellows, caps, diaphragms, bags,heaters, or the like. In this regard, although the illustratedembodiment of the infusion pump utilizes a coaxially aligned drivetrain, the motor could be arranged in an offset or otherwise non-coaxialmanner, relative to the longitudinal axis of the reservoir 1005.

As best shown in FIG. 12, the drive screw 1009 mates with threads 1202internal to the slide 1006. When the motor assembly 1007 is powered andoperated, the drive screw 1009 rotates, and the slide 1006 is forced totranslate in the axial direction 1018. In an exemplary embodiment, theinfusion pump 1000 includes a sleeve 1011 to prevent the slide 1006 fromrotating when the drive screw 1009 of the drive system 1008 rotates.Thus, rotation of the drive screw 1009 causes the slide 1006 to extendor retract relative to the drive motor assembly 1007. When the fluidinfusion device is assembled and operational, the slide 1006 contactsthe plunger 1017 to engage the reservoir 1005 and control delivery offluid from the infusion pump 1000. In an exemplary embodiment, theshoulder portion 1015 of the slide 1006 contacts or otherwise engagesthe plunger 1017 to displace the plunger 1017 in the axial direction1018. In alternative embodiments, the slide 1006 may include a threadedtip 1013 capable of being detachably engaged with internal threads 1204on the plunger 1017 of the reservoir 1005, as described in detail inU.S. Pat. Nos. 6,248,093 and 6,485,465, which are incorporated byreference herein.

As illustrated in FIG. 11, the electronics assembly 1004 includescontrol electronics 1024 coupled to the display element 1026, with thehousing 1002 including a transparent window portion 1028 that is alignedwith the display element 1026 to allow the display 1026 to be viewed bythe user when the electronics assembly 1004 is disposed within theinterior 1014 of the housing 1002. The control electronics 1024generally represent the hardware, firmware, processing logic and/orsoftware (or combinations thereof) configured to control operation ofthe motor assembly 1007 and/or drive system 1008, as described ingreater detail below in the context of FIG. 13. Whether suchfunctionality is implemented as hardware, firmware, a state machine, orsoftware depends upon the particular application and design constraintsimposed on the embodiment. Those familiar with the concepts describedhere may implement such functionality in a suitable manner for eachparticular application, but such implementation decisions should not beinterpreted as being restrictive or limiting. In an exemplaryembodiment, the control electronics 1024 includes one or moreprogrammable controllers that may be programmed to control operation ofthe infusion pump 1000.

The motor assembly 1007 includes one or more electrical leads 1036adapted to be electrically coupled to the electronics assembly 1004 toestablish communication between the control electronics 1024 and themotor assembly 1007. In response to command signals from the controlelectronics 1024 that operate a motor driver (e.g., a power converter)to regulate the amount of power supplied to the motor from a powersupply, the motor actuates the drive train components of the drivesystem 1008 to displace the slide 1006 in the axial direction 1018 toforce fluid from the reservoir 1005 along a fluid path (including tubing1021 and an infusion set), thereby administering doses of the fluidcontained in the reservoir 1005 into the user's body. Preferably, thepower supply is realized one or more batteries contained within thehousing 1002. Alternatively, the power supply may be a solar panel,capacitor, AC or DC power supplied through a power cord, or the like. Insome embodiments, the control electronics 1024 may operate the motor ofthe motor assembly 1007 and/or drive system 1008 in a stepwise manner,typically on an intermittent basis; to administer discrete precise dosesof the fluid to the user according to programmed delivery profiles.

Referring to FIGS. 10-12, as described above, the user interface 1030includes HMI elements, such as buttons 1032 and a directional pad 1034,that are formed on a graphic keypad overlay 1031 that overlies a keypadassembly 1033, which includes features corresponding to the buttons1032, directional pad 1034 or other user interface items indicated bythe graphic keypad overlay 1031. When assembled, the keypad assembly1033 is coupled to the control electronics 1024, thereby allowing theHMI elements 1032, 1034 to be manipulated by the user to interact withthe control electronics 1024 and control operation of the infusion pump1000, for example, to administer a bolus of insulin, to change therapysettings, to change user preferences, to select display features, to setor disable alarms and reminders, and the like. In this regard, thecontrol electronics 1024 maintains and/or provides information to thedisplay 1026 regarding program parameters, delivery profiles, pumpoperation, alarms, warnings, statuses, or the like, which may beadjusted using the HMI elements 1032, 1034. In various embodiments, theHMI elements 1032, 1034 may be realized as physical objects (e.g.,buttons, knobs, joysticks, and the like) or virtual objects (e.g., usingtouch-sensing and/or proximity-sensing technologies). For example, insome embodiments, the display 1026 may be realized as a touch screen ortouch-sensitive display, and in such embodiments, the features and/orfunctionality of the HMI elements 1032, 1034 may be integrated into thedisplay 1026 and the HMI 1030 may not be present. In some embodiments,the electronics assembly 1004 may also include alert generating elementscoupled to the control electronics 1024 and suitably configured togenerate one or more types of feedback, such as, without limitation:audible feedback; visual feedback; haptic (physical) feedback; or thelike.

Referring to FIGS. 11-12, in accordance with one or more embodiments,the sensor assembly 1010 includes a back plate structure 1050 and aloading element 1060. The loading element 1060 is disposed between thecapping member 1012 and a beam structure 1070 that includes one or morebeams having sensing elements disposed thereon that are influenced bycompressive force applied to the sensor assembly 1010 that deflects theone or more beams, as described in greater detail in U.S. Pat. No.8,474,332, which is incorporated by reference herein. In exemplaryembodiments, the back plate structure 1050 is affixed, adhered, mounted,or otherwise mechanically coupled to the bottom surface 1038 of thedrive system 1008 such that the back plate structure 1050 residesbetween the bottom surface 1038 of the drive system 1008 and the housingcap 1016. The drive system capping member 1012 is contoured toaccommodate and conform to the bottom of the sensor assembly 1010 andthe drive system 1008. The drive system capping member 1012 may beaffixed to the interior of the housing 1002 to prevent displacement ofthe sensor assembly 1010 in the direction opposite the direction offorce provided by the drive system 1008 (e.g., the direction oppositedirection 1018). Thus, the sensor assembly 1010 is positioned betweenthe motor assembly 1007 and secured by the capping member 1012, whichprevents displacement of the sensor assembly 1010 in a downwarddirection opposite the direction of arrow 1018, such that the sensorassembly 1010 is subjected to a reactionary compressive force when thedrive system 1008 and/or motor assembly 1007 is operated to displace theslide 1006 in the axial direction 1018 in opposition to the fluidpressure in the reservoir 1005. Under normal operating conditions, thecompressive force applied to the sensor assembly 1010 is correlated withthe fluid pressure in the reservoir 1005. As shown, electrical leads1040 are adapted to electrically couple the sensing elements of thesensor assembly 1010 to the electronics assembly 1004 to establishcommunication to the control electronics 1024, wherein the controlelectronics 1024 are configured to measure, receive, or otherwise obtainelectrical signals from the sensing elements of the sensor assembly 1010that are indicative of the force applied by the drive system 1008 in theaxial direction 1018.

FIG. 13 depicts an exemplary embodiment of a control system 1300suitable for use with an infusion device 1302, such as any one of theinfusion devices 102, 902, 1000 described above. The control system 1300is capable of controlling or otherwise regulating a physiologicalcondition in the body 1301 of a user to a desired (or target) value orotherwise maintain the condition within a range of acceptable values inan automated or autonomous manner. In one or more exemplary embodiments,the condition being regulated is sensed, detected, measured or otherwisequantified by a sensing arrangement 1304 (e.g., sensing arrangement 904)communicatively coupled to the infusion device 1302. However, it shouldbe noted that in alternative embodiments, the condition being regulatedby the control system 1300 may be correlative to the measured valuesobtained by the sensing arrangement 1304. That said, for clarity andpurposes of explanation, the subject matter may be described herein inthe context of the sensing arrangement 1304 being realized as a glucosesensing arrangement that senses, detects, measures or otherwisequantifies the user's glucose level, which is being regulated in thebody 1301 of the user by the control system 1300.

In exemplary embodiments, the sensing arrangement 1304 includes one ormore interstitial glucose sensing elements that generate or otherwiseoutput electrical signals having a signal characteristic that iscorrelative to, influenced by, or otherwise indicative of the relativeinterstitial fluid glucose level in the body 1301 of the user. Theoutput electrical signals are filtered or otherwise processed to obtaina measurement value indicative of the user's interstitial fluid glucoselevel. In exemplary embodiments, a blood glucose meter 1330, such as afinger stick device, is utilized to directly sense, detect, measure orotherwise quantify the blood glucose in the body 1301 of the user. Inthis regard, the blood glucose meter 1330 outputs or otherwise providesa measured blood glucose value that may be utilized as a referencemeasurement for calibrating the sensing arrangement 1304 and convertinga measurement value indicative of the user's interstitial fluid glucoselevel into a corresponding calibrated blood glucose value. For purposesof explanation, the calibrated blood glucose value calculated based onthe electrical signals output by the sensing element(s) of the sensingarrangement 1304 may alternatively be referred to herein as the sensorglucose value, the sensed glucose value, or variants thereof.

In the illustrated embodiment, the pump control system 1320 generallyrepresents the electronics and other components of the infusion device1302 that control operation of the fluid infusion device 1302 accordingto a desired infusion delivery program in a manner that is influenced bythe sensed glucose value indicative of a current glucose level in thebody 1301 of the user. For example, to support a closed-loop operatingmode, the pump control system 1320 maintains, receives, or otherwiseobtains a target or commanded glucose value, and automatically generatesor otherwise determines dosage commands for operating an actuationarrangement, such as a motor 1307, to displace the plunger 1317 anddeliver insulin to the body 1301 of the user based on the differencebetween a sensed glucose value and the target glucose value. In otheroperating modes, the pump control system 1320 may generate or otherwisedetermine dosage commands configured to maintain the sensed glucosevalue below an upper glucose limit, above a lower glucose limit, orotherwise within a desired range of glucose values. In practice, theinfusion device 1302 may store or otherwise maintain the target value,upper and/or lower glucose limit(s), and/or other glucose thresholdvalue(s) in a data storage element accessible to the pump control system1320.

The target glucose value and other threshold glucose values may bereceived from an external component (e.g., CCD 906 and/or computingdevice 908) or be input by a user via a user interface element 1340associated with the infusion device 1302. In practice, the one or moreuser interface element(s) 1340 associated with the infusion device 1302typically include at least one input user interface element, such as,for example, a button, a keypad, a keyboard, a knob, a joystick, amouse, a touch panel, a touchscreen, a microphone or another audio inputdevice, and/or the like. Additionally, the one or more user interfaceelement(s) 1340 include at least one output user interface element, suchas, for example, a display element (e.g., a light-emitting diode or thelike), a display device (e.g., a liquid crystal display or the like), aspeaker or another audio output device, a haptic feedback device, or thelike, for providing notifications or other information to the user. Itshould be noted that although FIG. 13 depicts the user interfaceelement(s) 1340 as being separate from the infusion device 1302, inpractice, one or more of the user interface element(s) 1340 may beintegrated with the infusion device 1302. Furthermore, in someembodiments, one or more user interface element(s) 1340 are integratedwith the sensing arrangement 1304 in addition to and/or in alternativeto the user interface element(s) 1340 integrated with the infusiondevice 1302. The user interface element(s) 1340 may be manipulated bythe user to operate the infusion device 1302 to deliver correctionboluses, adjust target and/or threshold values, modify the deliverycontrol scheme or operating mode, and the like, as desired.

Still referring to FIG. 13, in the illustrated embodiment, the infusiondevice 1302 includes a motor control module 1312 coupled to a motor 1307(e.g., motor assembly 1007) that is operable to displace a plunger 1317(e.g., plunger 1017) in a reservoir (e.g., reservoir 1005) and provide adesired amount of fluid to the body 1301 of a user. In this regard,displacement of the plunger 1317 results in the delivery of a fluid thatis capable of influencing the condition in the body 1301 of the user tothe body 1301 of the user via a fluid delivery path (e.g., via tubing1021 of an infusion set 1025). A motor driver module 1314 is coupledbetween an energy source 1303 and the motor 1307. The motor controlmodule 1312 is coupled to the motor driver module 1314, and the motorcontrol module 1312 generates or otherwise provides command signals thatoperate the motor driver module 1314 to provide current (or power) fromthe energy source 1303 to the motor 1307 to displace the plunger 1317 inresponse to receiving, from a pump control system 1320, a dosage commandindicative of the desired amount of fluid to be delivered.

In exemplary embodiments, the energy source 1303 is realized as abattery housed within the infusion device 1302 (e.g., within housing1002) that provides direct current (DC) power. In this regard, the motordriver module 1314 generally represents the combination of circuitry,hardware and/or other electrical components configured to convert orotherwise transfer DC power provided by the energy source 1303 intoalternating electrical signals applied to respective phases of thestator windings of the motor 1307 that result in current flowing throughthe stator windings that generates a stator magnetic field and causesthe rotor of the motor 1307 to rotate. The motor control module 1312 isconfigured to receive or otherwise obtain a commanded dosage from thepump control system 1320, convert the commanded dosage to a commandedtranslational displacement of the plunger 1317, and command, signal, orotherwise operate the motor driver module 1314 to cause the rotor of themotor 1307 to rotate by an amount that produces the commandedtranslational displacement of the plunger 1317. For example, the motorcontrol module 1312 may determine an amount of rotation of the rotorrequired to produce translational displacement of the plunger 1317 thatachieves the commanded dosage received from the pump control system1320. Based on the current rotational position (or orientation) of therotor with respect to the stator that is indicated by the output of therotor sensing arrangement 1316, the motor control module 1312 determinesthe appropriate sequence of alternating electrical signals to be appliedto the respective phases of the stator windings that should rotate therotor by the determined amount of rotation from its current position (ororientation). In embodiments where the motor 1307 is realized as a BLDCmotor, the alternating electrical signals commutate the respectivephases of the stator windings at the appropriate orientation of therotor magnetic poles with respect to the stator and in the appropriateorder to provide a rotating stator magnetic field that rotates the rotorin the desired direction. Thereafter, the motor control module 1312operates the motor driver module 1314 to apply the determinedalternating electrical signals (e.g., the command signals) to the statorwindings of the motor 1307 to achieve the desired delivery of fluid tothe user.

When the motor control module 1312 is operating the motor driver module1314, current flows from the energy source 1303 through the statorwindings of the motor 1307 to produce a stator magnetic field thatinteracts with the rotor magnetic field. In some embodiments, after themotor control module 1312 operates the motor driver module 1314 and/ormotor 1307 to achieve the commanded dosage, the motor control module1312 ceases operating the motor driver module 1314 and/or motor 1307until a subsequent dosage command is received. In this regard, the motordriver module 1314 and the motor 1307 enter an idle state during whichthe motor driver module 1314 effectively disconnects or isolates thestator windings of the motor 1307 from the energy source 1303. In otherwords, current does not flow from the energy source 1303 through thestator windings of the motor 1307 when the motor 1307 is idle, and thus,the motor 1307 does not consume power from the energy source 1303 in theidle state, thereby improving efficiency.

Depending on the embodiment, the motor control module 1312 may beimplemented or realized with a general purpose processor, amicroprocessor, a controller, a microcontroller, a state machine, acontent addressable memory, an application specific integrated circuit,a field programmable gate array, any suitable programmable logic device,discrete gate or transistor logic, discrete hardware components, or anycombination thereof, designed to perform the functions described herein.In exemplary embodiments, the motor control module 1312 includes orotherwise accesses a data storage element or memory, including any sortof random access memory (RAM), read only memory (ROM), flash memory,registers, hard disks, removable disks, magnetic or optical massstorage, or any other short or long term storage media or othernon-transitory computer-readable medium, which is capable of storingprogramming instructions for execution by the motor control module 1312.The computer-executable programming instructions, when read and executedby the motor control module 1312, cause the motor control module 1312 toperform or otherwise support the tasks, operations, functions, andprocesses described herein.

It should be appreciated that FIG. 13 is a simplified representation ofthe infusion device 1302 for purposes of explanation and is not intendedto limit the subject matter described herein in any way. In this regard,depending on the embodiment, some features and/or functionality of thesensing arrangement 1304 may implemented by or otherwise integrated intothe pump control system 1320, or vice versa. Similarly, in practice, thefeatures and/or functionality of the motor control module 1312 mayimplemented by or otherwise integrated into the pump control system1320, or vice versa. Furthermore, the features and/or functionality ofthe pump control system 1320 may be implemented by control electronics1024 located in the fluid infusion device 1302, while in alternativeembodiments, the pump control system 1320 may be implemented by a remotecomputing device that is physically distinct and/or separate from theinfusion device 1302, such as, for example, the CCD 906 or the computingdevice 908.

FIG. 14 depicts an exemplary embodiment of a pump control system 1400suitable for use as the pump control system 1320 in FIG. 13 inaccordance with one or more embodiments. The illustrated pump controlsystem 1400 includes, without limitation, a pump control module 1402, acommunications interface 1404, and a data storage element (or memory)1406. The pump control module 1402 is coupled to the communicationsinterface 1404 and the memory 1406, and the pump control module 1402 issuitably configured to support the operations, tasks, and/or processesdescribed herein. In exemplary embodiments, the pump control module 1402is also coupled to one or more user interface elements 1408 (e.g., userinterface 1030, 1340) for receiving user input and providingnotifications, alerts, or other therapy information to the user.Although FIG. 14 depicts the user interface element 1408 as beingseparate from the pump control system 1400, in various alternativeembodiments, the user interface element 1408 may be integrated with thepump control system 1400 (e.g., as part of the infusion device 1302),the sensing arrangement 1304 or another element of an infusion system900 (e.g., the computer 908 or CCD 906).

Referring to FIG. 14 and with reference to FIG. 13, the communicationsinterface 1404 generally represents the hardware, circuitry, logic,firmware and/or other components of the pump control system 1400 thatare coupled to the pump control module 1402 and configured to supportcommunications between the pump control system 1400 and the sensingarrangement 1304. In this regard, the communications interface 1404 mayinclude or otherwise be coupled to one or more transceiver modulescapable of supporting wireless communications between the pump controlsystem 1320, 1400 and the sensing arrangement 1304 or another electronicdevice 106, 110, 800, 812, 906, 908 in an infusion system 900 or amanagement system 100, 816. For example, the communications interface1404 may be utilized to receive sensor measurement values or othermeasurement data from a sensing arrangement 904, 1304 as well as uploadsuch sensor measurement values to a server 106 or other computing device110, 800, 812, 908 for purposes of detecting meals or other events andgenerating reports and related GUI displays as described above. In otherembodiments, the communications interface 1404 may be configured tosupport wired communications to/from the sensing arrangement 1304.

The pump control module 1402 generally represents the hardware,circuitry, logic, firmware and/or other component of the pump controlsystem 1400 that is coupled to the communications interface 1404 andconfigured to determine dosage commands for operating the motor 1306 todeliver fluid to the body 1301 based on data received from the sensingarrangement 1304 and perform various additional tasks, operations,functions and/or operations described herein. For example, in exemplaryembodiments, pump control module 1402 implements or otherwise executes acommand generation application 1410 that supports one or more autonomousoperating modes and calculates or otherwise determines dosage commandsfor operating the motor 1306 of the infusion device 1302 in anautonomous operating mode based at least in part on a currentmeasurement value for a condition in the body 1301 of the user. Forexample, in a closed-loop operating mode, the command generationapplication 1410 may determine a dosage command for operating the motor1306 to deliver insulin to the body 1301 of the user based at least inpart on the current glucose measurement value most recently receivedfrom the sensing arrangement 1304 to regulate the user's blood glucoselevel to a target reference glucose value. Additionally, the commandgeneration application 1410 may generate dosage commands for bolusesthat are manually-initiated or otherwise instructed by a user via a userinterface element 1408.

Still referring to FIG. 14, depending on the embodiment, the pumpcontrol module 1402 may be implemented or realized with a generalpurpose processor, a microprocessor, a controller, a microcontroller, astate machine, a content addressable memory, an application specificintegrated circuit, a field programmable gate array, any suitableprogrammable logic device, discrete gate or transistor logic, discretehardware components, or any combination thereof, designed to perform thefunctions described herein. In this regard, the steps of a method oralgorithm described in connection with the embodiments disclosed hereinmay be embodied directly in hardware, in firmware, in a software moduleexecuted by the pump control module 1402, or in any practicalcombination thereof. In exemplary embodiments, the pump control module1402 includes or otherwise accesses the data storage element or memory1406, which may be realized using any sort of non-transitorycomputer-readable medium capable of storing programming instructions forexecution by the pump control module 1402. The computer-executableprogramming instructions, when read and executed by the pump controlmodule 1402, cause the pump control module 1402 to implement orotherwise generate the command generation application 1410 and performtasks, operations, functions, and processes described herein.

It should be understood that FIG. 14 is a simplified representation of apump control system 1400 for purposes of explanation and is not intendedto limit the subject matter described herein in any way. For example, insome embodiments, the features and/or functionality of the motor controlmodule 1312 may be implemented by or otherwise integrated into the pumpcontrol system 1400 and/or the pump control module 1402, for example, bythe command generation application 1410 converting the dosage commandinto a corresponding motor command, in which case, the separate motorcontrol module 1312 may be absent from an embodiment of the infusiondevice 1302.

FIG. 15 depicts an exemplary closed-loop control system 1500 that may beimplemented by a pump control system 1320, 1400 to provide a closed-loopoperating mode that autonomously regulates a condition in the body of auser to a reference (or target) value. It should be appreciated thatFIG. 15 is a simplified representation of the control system 1500 forpurposes of explanation and is not intended to limit the subject matterdescribed herein in any way.

In exemplary embodiments, the control system 1500 receives or otherwiseobtains a target glucose value at input 1502. In some embodiments, thetarget glucose value may be stored or otherwise maintained by theinfusion device 1302 (e.g., in memory 1406), however, in somealternative embodiments, the target value may be received from anexternal component (e.g., CCD 906 and/or computer 908). In one or moreembodiments, the target glucose value may be dynamically calculated orotherwise determined prior to entering the closed-loop operating modebased on one or more patient-specific control parameters. For example,the target blood glucose value may be calculated based at least in parton a patient-specific reference basal rate and a patient-specific dailyinsulin requirement, which are determined based on historical deliveryinformation over a preceding interval of time (e.g., the amount ofinsulin delivered over the preceding 24 hours). The control system 1500also receives or otherwise obtains a current glucose measurement value(e.g., the most recently obtained sensor glucose value) from the sensingarrangement 1304 at input 1504. The illustrated control system 1500implements or otherwise provides proportional-integral-derivative (PID)control to determine or otherwise generate delivery commands foroperating the motor 1310 based at least in part on the differencebetween the target glucose value and the current glucose measurementvalue. In this regard, the PID control attempts to minimize thedifference between the measured value and the target value, and therebyregulates the measured value to the desired value. PID controlparameters are applied to the difference between the target glucoselevel at input 1502 and the measured glucose level at input 1504 togenerate or otherwise determine a dosage (or delivery) command providedat output 1530. Based on that delivery command, the motor control module1312 operates the motor 1310 to deliver insulin to the body of the userto influence the user's glucose level, and thereby reduce the differencebetween a subsequently measured glucose level and the target glucoselevel.

The illustrated control system 1500 includes or otherwise implements asummation block 1506 configured to determine a difference between thetarget value obtained at input 1502 and the measured value obtained fromthe sensing arrangement 1304 at input 1504, for example, by subtractingthe target value from the measured value. The output of the summationblock 1506 represents the difference between the measured and targetvalues, which is then provided to each of a proportional term path, anintegral term path, and a derivative term path. The proportional termpath includes a gain block 1520 that multiplies the difference by aproportional gain coefficient, K_(P), to obtain the proportional term.The integral term path includes an integration block 1508 thatintegrates the difference and a gain block 1522 that multiplies theintegrated difference by an integral gain coefficient, K_(I), to obtainthe integral term. The derivative term path includes a derivative block1510 that determines the derivative of the difference and a gain block1524 that multiplies the derivative of the difference by a derivativegain coefficient, K_(D), to obtain the derivative term. The proportionalterm, the integral term, and the derivative term are then added orotherwise combined to obtain a delivery command that is utilized tooperate the motor at output 1530. Various implementation detailspertaining to closed-loop PID control and determine gain coefficientsare described in greater detail in U.S. Pat. No. 7,402,153, which isincorporated by reference.

In one or more exemplary embodiments, the PID gain coefficients areuser-specific (or patient-specific) and dynamically calculated orotherwise determined prior to entering the closed-loop operating modebased on historical insulin delivery information (e.g., amounts and/ortimings of previous dosages, historical correction bolus information, orthe like), historical sensor measurement values, historical referenceblood glucose measurement values, user-reported or user-input events(e.g., meals, exercise, and the like), and the like. In this regard, oneor more patient-specific control parameters (e.g., an insulinsensitivity factor, a daily insulin requirement, an insulin limit, areference basal rate, a reference fasting glucose, an active insulinaction duration, pharmodynamical time constants, or the like) may beutilized to compensate, correct, or otherwise adjust the PID gaincoefficients to account for various operating conditions experiencedand/or exhibited by the infusion device 1302. The PID gain coefficientsmay be maintained by the memory 1406 accessible to the pump controlmodule 1402. In this regard, the memory 1406 may include a plurality ofregisters associated with the control parameters for the PID control.For example, a first parameter register may store the target glucosevalue and be accessed by or otherwise coupled to the summation block1506 at input 1502, and similarly, a second parameter register accessedby the proportional gain block 1520 may store the proportional gaincoefficient, a third parameter register accessed by the integration gainblock 1522 may store the integration gain coefficient, and a fourthparameter register accessed by the derivative gain block 1524 may storethe derivative gain coefficient.

Referring to FIGS. 13-15, in one or more embodiments, thepatient-specific meal detection model may be stored or otherwisemaintained by the pump control system 1320, 1400 (e.g., in memory 1406)to support the infusion device 1302 performing the meal detectionprocess 600 substantially in real-time. In this regard, the pump controlsystem 1320, 1400 may continually monitor the sensor glucose measurementvalues received from the sensing arrangement 1304 and apply the mealdetection model to the most recent set of sensor glucose measurementvalues (e.g., buffered sensor glucose measurements corresponding to thepreceding hour) to detect or otherwise identify whether the sensorglucose measurements indicate that the patient has consumed a meal. Inresponse to detecting a meal, the pump control system 1320, 1400 maytag, mark, or otherwise provide an indication associated with one ormore sensor glucose measurement values prior to uploading the sensorglucose measurement values to a server (e.g., server 106). Additionally,in some embodiments, the pump control system 1320, 1400 mayautomatically adjust one or more aspects of the autonomous operation ofthe infusion device 1302 to temporarily modify delivery of the infusiondevice 1302 to account for the patient consuming a meal. For example,one or more of the closed-loop gain coefficients 1520, 1522, 1524 may betemporarily modified to alter the response of the closed-loop controlsystem 1500 in a manner that more effectively regulates or moderates thepatient's glucose level after consuming a meal (e.g., to prevent apostprandial hyperglycemic or hypoglycemic event). In this regard, itshould be appreciated that the patient-specific meal detection modelsmay be employed in any number of different ways depending on the needsof a particular application, and the subject matter described herein isnot limited to any particular implementation described herein.

It should be noted that although the subject matter may be describedherein primarily in the context of an infusion device delivering insulinto the body of a patient with diabetes to regulate the patient's glucoselevel for purposes of explanation, in practice, the subject matter isnot limited to use with infusion devices, insulin, diabetes or glucosecontrol, and the like. Rather, the subject matter may be implemented inan equivalent manner in the context of patient management systems thatdo not include an infusion device, for example, in systems with wherepatients self-administer injections, oral medications, or the like, insystems where a sensing arrangement is utilized to monitor any aphysiological condition of a patient in a substantially continuousmanner, or in the context of a patient with dysglycemia or anotherphysiological condition being monitored that is influenced by meals orother behavioral events.

For the sake of brevity, conventional techniques related to glucosesensing and/or monitoring, bolusing, meal boluses or correction boluses,and other functional aspects of the subject matter may not be describedin detail herein. In addition, certain terminology may also be used inthe herein for the purpose of reference only, and thus is not intendedto be limiting. For example, terms such as “first”, “second”, and othersuch numerical terms referring to structures do not imply a sequence ororder unless clearly indicated by the context. The foregoing descriptionmay also refer to elements or nodes or features being “connected” or“coupled” together. As used herein, unless expressly stated otherwise,“coupled” means that one element/node/feature is directly or indirectlyjoined to (or directly or indirectly communicates with) anotherelement/node/feature, and not necessarily mechanically.

While at least one exemplary embodiment has been presented in theforegoing detailed description, it should be appreciated that a vastnumber of variations exist. It should also be appreciated that theexemplary embodiment or embodiments described herein are not intended tolimit the scope, applicability, or configuration of the claimed subjectmatter in any way. For example, the subject matter described herein isnot necessarily limited to the infusion devices and related systemsdescribed herein. Moreover, the foregoing detailed description willprovide those skilled in the art with a convenient road map forimplementing the described embodiment or embodiments. It should beunderstood that various changes can be made in the function andarrangement of elements without departing from the scope defined by theclaims, which includes known equivalents and foreseeable equivalents atthe time of filing this patent application. Accordingly, details of theexemplary embodiments or other limitations described above should not beread into the claims absent a clear intention to the contrary.

What is claimed is:
 1. A method of detecting meals pertaining tooperation of a medical device associated with a patient, the methodcomprising: obtaining, by a computing device via a network, a pluralityof glucose measurements indicative of a glucose level in a body of thepatient during an analysis interval; obtaining, by the computing device,a patient-specific meal detection model that identifies one or moreglucose measurement statistics that are correlative to occurrence of ameal for the patient; determining, by the computing device, values forthe one or more glucose measurement statistics characterizing theglucose level in the body of the patient within the analysis intervalbased on the plurality of glucose measurements; determining, by thecomputing device, a meal probability associated with the analysisinterval based on historical event data associated with the patient;determining, by the computing device, a meal consumption metric based onthe values for the one or more glucose measurement statistics and themeal probability using respective correlation coefficient values fromthe patient-specific meal detection model; autonomously detecting, bythe computing device, an occurrence of a meal during the analysisinterval based on the meal consumption metric; and in response todetecting the occurrence of the meal, providing, by the computingdevice, an indication of the occurrence of the meal associated with theanalysis interval.
 2. The method of claim 1, further comprisinggenerating the patient-specific meal detection model based on historicalmeasurements indicative of the glucose level in the body of the patientand the historical event data.
 3. The method of claim 2, whereingenerating the patient-specific meal detection model comprises:determining a plurality of glucose measurement statistics based on thehistorical measurements; determining a predictive subset of theplurality of glucose measurement statistics for the patient based on arespective correlation between a respective glucose measurementstatistic of the plurality of glucose measurement statistics and thehistorical event data; and determining the respective correlationcoefficients associated with respective ones of the predictive subset ofglucose measurement statistics.
 4. The method of claim 3, wherein: theone or more glucose measurement statistics comprise the predictivesubset of glucose measurement statistics for the analysis interval. 5.The method of claim 1, the historical event data comprising meal bolusdata associated with the patient, wherein: determining the mealprobability comprises determining the meal probability for the analysisinterval based on the meal bolus data associated with the patient; andproviding the indication comprises establishing an association betweenthe occurrence of the meal and the analysis interval.
 6. The method ofclaim 1, wherein providing the indication comprises updating thehistorical event data to indicate the occurrence of the meal associatedwith the analysis interval.
 7. The method of claim 1, wherein providingthe indication comprises storing an indicator of the occurrence of themeal in association with the one or more glucose measurements.
 8. Themethod of claim 1, further comprising determining a timestamp for theoccurrence of the meal based on the one or more glucose measurements,wherein providing the indication comprises storing meal indicator havingthe timestamp associated therewith.
 9. The method of claim 1, whereinproviding the indication comprises providing a pattern guidance displayfor an event pattern that is influenced by the occurrence of the mealassociated with the analysis interval.
 10. A computer-readable mediumhaving instructions stored thereon that are executable by a processingsystem of the computing device to perform the method of claim
 1. 11. Amethod of detecting meals during to operation of an insulin infusiondevice associated with a patient, the method comprising: obtainingsensor glucose measurements for the patient; obtaining a bolus historyfor the patient; obtaining a meal detection model associated with thepatient, the meal detection model identifying a predictive subset ofglucose measurement statistics correlative to meals by the patient;calculating the predictive subset of glucose measurement statisticscharacterizing a glucose level of the patient for an analysis intervalof the sensor glucose measurements; determining a meal probabilityassociated with the analysis interval based on the bolus history;determining a meal consumption metric based on the calculated predictivesubset of glucose measurement statistics for the analysis interval ofthe sensor glucose measurements and the meal probability associated withthe analysis interval using respective correlation coefficient valuesfrom the meal detection model; autonomously detecting occurrence of ameal during the analysis interval based on the meal consumption metric;and in response to autonomously detecting occurrence of the meal,providing an indication of the occurrence of the meal associated withthe analysis interval.
 12. The method of claim 11, wherein providing theindication comprises updating the bolus history to indicate theoccurrence of the meal associated with the analysis interval.
 13. Themethod of claim 12, wherein providing the indication comprisesclassifying the analysis interval of the sensor glucose measurements asa meal segment.
 14. The method of claim 11, further comprisingdetermining the meal detection model associated with the patient basedon historical sensor glucose measurements for the patient correspondingto the bolus history.
 15. The method of claim 14, wherein determiningthe meal detection model comprises: classifying the historical sensorglucose measurements into meal and non-meal segments; and determining apredictive glucose measurement statistic of the predictive subset basedon a correlation between the meal segments and values of the predictiveglucose measurement statistic for portions of the historical sensorglucose measurements corresponding to the meal segments.
 16. The methodof claim 11, further comprising providing a pattern guidance displaycorresponding to an event pattern influenced by the occurrence of themeal and including a graphical indication of the occurrence of the mealassociated with a time of day corresponding to the analysis interval.17. A system comprising: a sensing arrangement to obtain glucosemeasurement values for a glucose level in a body of a patient; adatabase to store historical event data and a meal detection modelassociated with the patient; and a computing device communicativelycoupled to the database and a network to: obtain the glucose measurementvalues; determine values for one or more glucose measurement statisticscharacterizing the glucose level in the body of the patient that arecorrelative to occurrence of a meal for the patient for an analysisinterval based on the glucose measurement values; determine a mealprobability associated with the analysis interval based on thehistorical event data associated with the patient; apply the mealdetection model to the one or more glucose measurement statistics andthe meal probability to determine a meal consumption metric based on thevalues for the one or more glucose measurement statistics and the mealprobability using respective correlation coefficient values from themeal detection model and autonomously detect occurrence of a meal duringthe analysis interval based on the meal consumption metric; and providean indication of the occurrence of the meal associated with the analysisinterval.
 18. The system of claim 17, further comprising an infusiondevice coupled to the sensing arrangement and the network, wherein theinfusion device is operable to deliver fluid to the body of the patientbased on the glucose measurement values and upload the glucosemeasurement values to the computing device.
 19. The system of claim 17,wherein the computing device generates a graphical user interfacedisplay on a client device coupled to the computing device over thenetwork, wherein the indication comprises a graphical indication of theoccurrence of the meal associated with the analysis interval on thegraphical user interface display.
 20. The method of claim 1, wherein theone or more glucose measurement statistics include at least one of amean sensor glucose measurement value over the analysis interval, astandard deviation of the plurality of glucose measurement values duringthe analysis interval, a mean rate of change of the plurality of glucosemeasurement values on a sample-to-sample basis during the analysisinterval, a standard deviation associated with the mean rate of changeof the plurality of glucose measurement values, an absolute amplitude ofthe plurality of glucose measurement values during the analysisinterval, and an amplitude difference between first and last glucosemeasurement values of the plurality of glucose measurement values duringthe analysis interval.